From 30f7544b04f23d59e96d684161fbb80f016f7bc6 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Thu, 26 Apr 2018 21:06:12 +0900 Subject: [PATCH 01/42] all --- x_ksh/optx/optx | 0 x_ksh/sound_analysis2.py | 162 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 162 insertions(+) create mode 100644 x_ksh/optx/optx create mode 100644 x_ksh/sound_analysis2.py diff --git a/x_ksh/optx/optx b/x_ksh/optx/optx new file mode 100644 index 0000000..e69de29 diff --git a/x_ksh/sound_analysis2.py b/x_ksh/sound_analysis2.py new file mode 100644 index 0000000..d60df7f --- /dev/null +++ b/x_ksh/sound_analysis2.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +import librosa +import soundfile as sf +from matplotlib import pyplot as plt +import numpy as np +import os +from sklearn.preprocessing import normalize +from tensorflow.python.ops.rnn_cell_impl import DropoutWrapper +path = '/Users/kimseunghyuck/desktop/audio_train/' +files=os.listdir(path) + +#show one sample file +filename = files[0] +y, sr = sf.read(path+filename, dtype='float32') +mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) +mfcc.shape #20,161 + +#show second sample file +filename = files[1] +y, sr = sf.read(path+filename, dtype='float32') +mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) +mfcc.shape #20,109 + +#show second sample file +filename = files[3] +y, sr = sf.read(path+filename, dtype='float32') +mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) +mfcc.shape #20,143 + +#show graph +plt.figure(figsize=(mfcc.shape[1]/15, 5)) +plt.plot(mfcc) + +#scaling for each frequency +max_mfcc=np.max(mfcc, axis=1) +mins, maxs=np.min(max_mfcc), np.max(max_mfcc) +scaled_mfcc=(max_mfcc-mins)/(maxs-mins) +scaled_mfcc + +#import labels +import tensorflow as tf +tf.set_random_seed(777) +import pandas as pd +train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') + +#train/test, Data/Label split +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(train, test_size = 0.3) +trainfile = train_set.values[:,0] +testfile = test_set.values[:,0] +trainLabel = train_set.values[:,1] +testLabel = test_set.values[:,1] + +#data load and extract mfcc (scaling indluded) +path = '/Users/kimseunghyuck/desktop/audio_train/' + +def data2array(file): + dic = {} + i=0 + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + max_mfcc=np.max(mfcc, axis=1) + mins, maxs=np.min(max_mfcc), np.max(max_mfcc) + scaled_mfcc=(max_mfcc-mins)/(maxs-mins) + dic[i] = scaled_mfcc + i+=1 + array=np.array(list(dic.values())) + return(array) + +trainData=data2array(trainfile) +testData=data2array(testfile) +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 20) (2842, 20) (6631,) (2842,) + +#how many kinds of label? +len(np.unique(trainLabel)) #41 +len(np.unique(testLabel)) #41 + +# +idx = np.unique(trainLabel) +trainLabel=list(trainLabel) +trainLabel=pd.Series(trainLabel) + +for i in range(len(idx)): + for j in trainLabel: + if idx[i]==j: + trainLabel.loc['Oboe'] +#여기서 실패 + +def one_hot(x): + lst=[] + for i in x: + lst.append(np.repeat(0,41)) + lst[-1][i]+=1 + return(lst) +trainLabel=one_hot(trainLabel) +validateLabel=one_hot(testLabel) +# + +#다시 돌릴때는 여기부터 +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.01 +training_epochs = 20 +batch_size = 100 +steps_for_validate = 5 + +#placeholders +X = tf.placeholder(tf.float32, [None, 20], name="X") +X1 = tf.reshape(X, [-1, 10, 10]) +Y = tf.placeholder(tf.string, [None, 1], name="Y") +keep_prob = tf.placeholder(tf.float32) + +#cells +cells = tf.nn.rnn_cell.MultiRNNCell([DropoutWrapper(tf.nn.rnn_cell.BasicLSTMCell(num_units=256), + output_keep_prob=keep_prob) for _ in range(3)]) +h0 = cells.zero_state(batch_size, dtype=tf.float32) +output, hs = tf.nn.dynamic_rnn(cells, inputs=X1, initial_state=h0) +L1 = output[:, -1, :] + +W2 = tf.Variable(tf.random_normal([256, 41])) +b2 = tf.Variable(tf.random_normal([41])) +hypothesis = tf.matmul(L1, W2) + b2 + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( + logits=hypothesis, labels=Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: testData, Y: testLabel.reshape(-1, 1), keep_prob: 1})) + save_path = saver.save(sess, '/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/승혁/optx/optx') +print('Finished!') \ No newline at end of file From 7c42df3389932ea57112087beaabb3b6d7a61f02 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Thu, 26 Apr 2018 23:25:14 +0900 Subject: [PATCH 02/42] all --- x_ksh/optx/checkpoint | 2 + x_ksh/optx/optx.data-00000-of-00001 | Bin 0 -> 554996 bytes x_ksh/optx/optx.index | Bin 0 -> 695 bytes x_ksh/optx/optx.meta | Bin 0 -> 227710 bytes x_ksh/sound_analysis2.py | 73 ++++++++++++++-------------- 5 files changed, 39 insertions(+), 36 deletions(-) create mode 100644 x_ksh/optx/checkpoint create mode 100644 x_ksh/optx/optx.data-00000-of-00001 create mode 100644 x_ksh/optx/optx.index create mode 100644 x_ksh/optx/optx.meta diff --git a/x_ksh/optx/checkpoint b/x_ksh/optx/checkpoint new file mode 100644 index 0000000..385e2b0 --- /dev/null +++ b/x_ksh/optx/checkpoint @@ -0,0 +1,2 @@ +model_checkpoint_path: "/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/optx/optx" +all_model_checkpoint_paths: "/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/optx/optx" diff --git 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z+z&s9{?K|*X+5a)A5=yjM1N>KXl6ZV=09i_c@X`f^`N=+pt=8`dE`O#hxhJ^8`V(m zpFSDi5ZA<&R}K#z-}@C>41HIR>;G|Q{J-7#&2f$N;%~@t6>o~$Z*49Pru$D$zqdJ~ z2pPjaRft%hHR1nZ{NB;Y(Z$ihF=71p>4w6td1_%lwfU#@RP`A?Rek0@^%sPh6lC~O zYws$|-^}Y9={dFgJ3`MG)dQ<}KJqN7&s|IE^N^({;sw>9=4+K#FL@s-Yk}W1_CD?ZO~@kj;y%rw zu5a~6%9`=H`P}oZ`8;F^eXGCb_@+Ko){JlZH=b|$HzAAY8%^r2Z}msYn(?jq-1DvZ zJY)%dtH0*>ran~GjBolko^Sd$A&cl6 Date: Thu, 26 Apr 2018 23:25:53 +0900 Subject: [PATCH 03/42] all --- x_ksh/sound_analysis2.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/x_ksh/sound_analysis2.py b/x_ksh/sound_analysis2.py index 4b0d218..2cf1975 100644 --- a/x_ksh/sound_analysis2.py +++ b/x_ksh/sound_analysis2.py @@ -11,8 +11,6 @@ from matplotlib import pyplot as plt import numpy as np import os -from sklearn.preprocessing import normalize -from tensorflow.python.ops.rnn_cell_impl import DropoutWrapper path = '/Users/kimseunghyuck/desktop/audio_train/' files=os.listdir(path) From f7fb8ed7ac036d4cb7e083157d291d08d90e4c56 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Thu, 26 Apr 2018 23:37:48 +0900 Subject: [PATCH 04/42] all --- x_ksh/README.md | 24 ++++++++---------------- x_ksh/sound_analysis2.py | 2 +- 2 files changed, 9 insertions(+), 17 deletions(-) diff --git a/x_ksh/README.md b/x_ksh/README.md index fe35ac6..8998c7f 100644 --- a/x_ksh/README.md +++ b/x_ksh/README.md @@ -1,22 +1,14 @@ # Smallerhand; 김승혁 (KYLius-method)

내가 생각하는 주제...

-
-1. DCGAN(Deep Convolutional Generative Adversarial Network) + OpenCV 이용,
-새로운 외계인 형상 만듬.
-
-2. LSTM 또는 CNN + OpenCV 이용,
-년도별 인기있었던 남/녀 이미지 사진 모아서 어떤 연예인이 다음해에 인기있을지 예측
-(시계열 분석 혹은 현재 인기있는 연예인들과 비슷한 연예인을 찾음)
+진행상황
+sound_analysis2.py - 이거 하나만 보면 됨.
+
+import tensorflow as tf
+
+기준으로 앞부분은 데이터 디스플레이만 해봄.
+그다음은 
 
 
-### 아래는 읽고있는 링크 -
-https://m.blog.naver.com/PostView.nhn?blogId=atelierjpro&logNo=220984758512&proxyReferer=https%3A%2F%2Fwww.google.co.kr%2F -
-http://solarisailab.com/deep-learning -
-http://daddynkidsmakers.blogspot.kr/2017/07/gan-image-to-image.html -
-https://stackoverflow.com/questions/10357992/how-to-generate-audio-from-a-numpy-array?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa + diff --git a/x_ksh/sound_analysis2.py b/x_ksh/sound_analysis2.py index 2cf1975..5ab4d8c 100644 --- a/x_ksh/sound_analysis2.py +++ b/x_ksh/sound_analysis2.py @@ -82,7 +82,7 @@ def data2array(file): print(len(np.unique(trainLabel))) #41 print(len(np.unique(testLabel))) #41 -# +#label string -> integer(0~40) idx = np.unique(trainLabel) def Labeling(label): From 534d8b6ef98fce023d0ec1a00743826841490f95 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Thu, 26 Apr 2018 23:47:28 +0900 Subject: [PATCH 05/42] all --- x_ksh/README.md | 33 +++++++++++++++++++++++++++- x_ksh/optx/optx.data-00000-of-00001 | Bin 554996 -> 554996 bytes x_ksh/optx/optx.index | Bin 695 -> 695 bytes x_ksh/optx/optx.meta | Bin 227710 -> 103197 bytes 4 files changed, 32 insertions(+), 1 deletion(-) diff --git a/x_ksh/README.md b/x_ksh/README.md index 8998c7f..227d081 100644 --- a/x_ksh/README.md +++ b/x_ksh/README.md @@ -7,8 +7,39 @@ sound_analysis2.py - 이거 하나만 보면 됨. import tensorflow as tf 기준으로 앞부분은 데이터 디스플레이만 해봄. -그다음은 +그 뒷부분만 보면 됨. +기본 구조는 +1. 데이터 전처리 1 + +y, sr = soundfile.read(path+filename, dtype='float32') +mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) +max_mfcc=np.max(mfcc, axis=1) +mins, maxs=np.min(max_mfcc), np.max(max_mfcc) +scaled_mfcc=(max_mfcc-mins)/(maxs-mins) +dic[i] = scaled_mfcc + +soundfile.read 로 y, sr 추출 +librosa.feature.mfcc 로 mfcc 변환 (n_mfcc=20) +20개 속성들 각각의 max값을 뽑음 +전체를 min, max scaling +-> 이렇게 해서 하나의 sound 파일 당 20개의 값이 얻어짐. + +2. 데이터 전처리 2 +라벨은 0~40의 숫자로 바꿈 + +3. 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z+z&s9{?K|*X+5a)A5=yjM1N>KXl6ZV=09i_c@X`f^`N=+pt=8`dE`O#hxhJ^8`V(m zpFSDi5ZA<&R}K#z-}@C>41HIR>;G|Q{J-7#&2f$N;%~@t6>o~$Z*49Pru$D$zqdJ~ z2pPjaRft%hHR1nZ{NB;Y(Z$ihF=71p>4w6td1_%lwfU#@RP`A?Rek0@^%sPh6lC~O zYws$|-^}Y9={dFgJ3`MG)dQ<}KJqN7&s|IE^N^({;sw>9=4+K#FL@s-Yk}W1_CD?ZO~@kj;y%rw zu5a~6%9`=H`P}oZ`8;F^eXGCb_@+Ko){JlZH=b|$HzAAY8%^r2Z}msYn(?jq-1DvZ zJY)%dtH0*>ran~GjBolko^Sd$A&cl6 Date: Thu, 26 Apr 2018 23:52:54 +0900 Subject: [PATCH 06/42] all --- x_ksh/README.md | 13 +++++++++++++ x_ksh/optx/optx.data-00000-of-00001 | Bin 554996 -> 554996 bytes x_ksh/optx/optx.index | Bin 695 -> 695 bytes x_ksh/optx/optx.meta | Bin 103197 -> 227710 bytes x_ksh/sound_analysis2.py | 4 ++-- 5 files changed, 15 insertions(+), 2 deletions(-) diff --git a/x_ksh/README.md b/x_ksh/README.md index 227d081..86cfc8a 100644 --- a/x_ksh/README.md +++ b/x_ksh/README.md @@ -37,9 +37,22 @@ librosa.feature.mfcc 로 mfcc 변환 (n_mfcc=20) 소프트맥스, 아담으로 돌림. 4. 돌려봄 +1) lr=0.001 epoch = 1000 +keep_prob = 1 정확도 32.4~32.9% + +2) +다른 조건 같음 +keep_prob = 0.8 (트레이닝때만) +정확도 36.9~37.9% + +5. 개선할 부분 +파라미터를 좀 바꾸면 좀더 높아질지도 모르지만, +음색을 구분하려면, 그리고 소리에 담긴 뉘앙스를 구분하려면 아무래도 +시간적으로 연속적인 파형에서 단편적인 속성값만 뽑아내서는 안되고 연속적인 인식이 가능한 +CNN이나 RNN, LSTM을 써야 할 것이라고 생각함. diff --git a/x_ksh/optx/optx.data-00000-of-00001 b/x_ksh/optx/optx.data-00000-of-00001 index 86f27ed1d41cc66120e2c879a2bd5abd96b09e98..9de8809c873e073b59006fb93d0cff92ad0c2fef 100644 GIT binary patch delta 480667 zcmY(pc|4WR|NqZP5y`&G5+W^>Ea#l-nvqttXi+LjMSI#+q?28iY$3ACl2lURoa;LC zJe5jXXh)K?N!qkfQ9pV8zP)ds`OovrT-VGz?(=*;=Xv@1(fI2}W5MKcCG;|8EZx6i zFD}joqL*Rv6 z37+IrK)g<`qEoZOQFZBI+~iw^yiBw4Vv7rO#4dRhB9@ZO{ZI-+eMg`P5tqrcJLc#C z=O)m_>H^f>c7`tI$Ra_XC$Z8xOX@yz(5}ZaWU7ffBxx!`_Oduw6JtjCrKppni>pvR zzJzMD3~}H1i~JiSwuxEKB=PE2WAx~eK0ZS4r5Ej=iT28JaP*pHd_CtVN%mLwwBDU94o#q@>P z%i!M-4+ba2RO+8l{6RDVbsX|TuUw|V)3g$>IGP0G)(yko9SX_wq-^oWsGp=_|0r0@ zoB~Ua9C5@aZ~P)>GOpc!fIO%RrOvBNL4C6~;qZPlTxnPXcLg72i#2g2troZmve(K= z&UWpDiszB|Y>Y2X{&fOhHmauVgpqj7_VeN%uXv&or!Lum_TVwb55(>}FW@OY`l$BO 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a/x_ksh/README.md +++ b/x_ksh/README.md @@ -1,6 +1,5 @@ # Smallerhand; 김승혁 (KYLius-method) -

내가 생각하는 주제...

-
+
 진행상황
 sound_analysis2.py - 이거 하나만 보면 됨.
 
@@ -10,7 +9,9 @@ import tensorflow as tf
 그 뒷부분만 보면 됨.
 
 기본 구조는
+
 1. 데이터 전처리 1
+
 
 y, sr = soundfile.read(path+filename, dtype='float32')
 mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
@@ -24,19 +25,28 @@ librosa.feature.mfcc 로 mfcc 변환 (n_mfcc=20)
 20개 속성들 각각의 max값을 뽑음
 전체를 min, max scaling
 -> 이렇게 해서 하나의 sound 파일 당 20개의 값이 얻어짐.
+
+ 2. 데이터 전처리 2 +
 라벨은 0~40의 숫자로 바꿈
+
+ 3. MLP 설계 +
 특별한 의미는 없이 히든레이어 하나, 인풋, 아웃풋도 각각 하나로 해서 3개 레이어 만듬.
 (처음엔 히든 2개인 총 4개 레이어로 해봤는데 돌릴수록 정확도가 떨어지는게 오버피팅 되는 것 같아서 3개로 줄임)
 첫번째 층: 20 -> 128
 두번째 층: 128 -> 256
 세번째 층: 256 -> 41
 소프트맥스, 아담으로 돌림.
+
+ 4. 돌려봄 +
 1)
 lr=0.001
 epoch = 1000
@@ -47,8 +57,11 @@ keep_prob = 1
 다른 조건 같음
 keep_prob = 0.8 (트레이닝때만)
 정확도 36.9~37.9%
+
+ 5. 개선할 부분 +
 파라미터를 좀 바꾸면 좀더 높아질지도 모르지만,
 음색을 구분하려면, 그리고 소리에 담긴 뉘앙스를 구분하려면 아무래도
 시간적으로 연속적인 파형에서 단편적인 속성값만 뽑아내서는 안되고 연속적인 인식이 가능한

From 8e5a231d59c743418835ddb6194617317e813dea Mon Sep 17 00:00:00 2001
From: smallerhand 
Date: Thu, 26 Apr 2018 23:59:02 +0900
Subject: [PATCH 08/42] all

---
 x_ksh/sound_analysis2.py |   4 +-
 x_ksh/sound_analysis3.py | 129 +++++++++++++++++++++++++++++++++++++++
 2 files changed, 131 insertions(+), 2 deletions(-)
 create mode 100644 x_ksh/sound_analysis3.py

diff --git a/x_ksh/sound_analysis2.py b/x_ksh/sound_analysis2.py
index 11ab577..635244b 100644
--- a/x_ksh/sound_analysis2.py
+++ b/x_ksh/sound_analysis2.py
@@ -99,8 +99,8 @@ def Labeling(label):
 tf.reset_default_graph()     #그래프 초기화
 
 # hyper parameters
-learning_rate = 0.001
-training_epochs = 1000
+learning_rate = 0.0001
+training_epochs = 5000
 batch_size = 100
 steps_for_validate = 5
 
diff --git a/x_ksh/sound_analysis3.py b/x_ksh/sound_analysis3.py
new file mode 100644
index 0000000..8dc34ad
--- /dev/null
+++ b/x_ksh/sound_analysis3.py
@@ -0,0 +1,129 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr 26 18:53:10 2018
+
+@author: kimseunghyuck
+"""
+
+import librosa
+import soundfile as sf
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+#import labels
+import tensorflow as tf
+tf.set_random_seed(777) 
+import pandas as pd
+train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv')
+
+#train/test, Data/Label split
+train_set, test_set = train_test_split(train, test_size = 0.3)
+trainfile = train_set.values[:,0]
+testfile = test_set.values[:,0]
+trainLabel = train_set.values[:,1]
+testLabel = test_set.values[:,1]
+
+#data load and extract mfcc (scaling indluded)
+path = '/Users/kimseunghyuck/desktop/audio_train/'
+
+def data2array(file):
+    dic = {}
+    i=0
+    for filename in file:
+        y, sr = sf.read(path+filename, dtype='float32')
+        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
+        max_mfcc=np.max(mfcc, axis=1)
+        mins, maxs=np.min(max_mfcc), np.max(max_mfcc)
+        scaled_mfcc=(max_mfcc-mins)/(maxs-mins)
+        dic[i] = scaled_mfcc
+        i+=1
+    array=np.array(list(dic.values()))
+    return(array)
+
+trainData=data2array(trainfile)
+testData=data2array(testfile)
+print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape)
+# (6631, 20) (2842, 20) (6631,) (2842,)
+
+#how many kinds of label?
+print(len(np.unique(trainLabel)))   #41
+print(len(np.unique(testLabel)))    #41
+
+#label string -> integer(0~40)
+idx = np.unique(trainLabel)
+
+def Labeling(label):
+    r=pd.Series(label)
+    for i in range(len(idx)):
+        r[r.values==idx[i]]=i
+    return(r)
+
+trainLabel=Labeling(trainLabel)
+testLabel=Labeling(testLabel)
+print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel))
+
+#다시 돌릴때는 여기부터 
+tf.reset_default_graph()     #그래프 초기화
+
+# hyper parameters
+learning_rate = 0.0001
+training_epochs = 5000
+batch_size = 100
+steps_for_validate = 5
+
+#placeholders
+X = tf.placeholder(tf.float32, [None, 20], name="X") 
+Y = tf.placeholder(tf.int32, [None, 1], name="Y")
+Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41])
+keep_prob = tf.placeholder(tf.float32)
+
+# weights & bias for nn layers
+W1 = tf.Variable(tf.random_normal([20, 128]))
+b1 = tf.Variable(tf.random_normal([128]))
+L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
+L1 = tf.nn.dropout(L1, keep_prob=keep_prob)
+
+W2 = tf.Variable(tf.random_normal([128, 256]))
+b2 = tf.Variable(tf.random_normal([256]))
+L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
+L2 = tf.nn.dropout(L2, keep_prob=keep_prob)
+
+W4 = tf.Variable(tf.random_normal([256, 41]))
+b4 = tf.Variable(tf.random_normal([41]))
+hypothesis = tf.matmul(L2, W4) + b4
+
+# define cost/loss & optimizer
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
+    logits=hypothesis, labels=Y_onehot))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+# initialize
+sess = tf.Session()
+sess.run(tf.global_variables_initializer())
+saver = tf.train.Saver()
+
+# train my model
+print('Learning started. It takes sometime.')
+for epoch in range(training_epochs):
+    avg_cost = 0
+    total_batch = int(len(trainData) / batch_size)
+    for i in range(total_batch):
+        batch_xs = trainData[i*batch_size:(i+1)*batch_size]
+        batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1)
+        feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: .8}
+        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
+        avg_cost += c / total_batch
+    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
+    if epoch % steps_for_validate == steps_for_validate-1:
+        correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y_onehot, 1))
+        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+        print('Accuracy:', sess.run(accuracy, feed_dict={
+                X: testData, Y: testLabel.reshape(-1, 1), keep_prob: 1}))
+        save_path = saver.save(sess, '/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/optx/optx')
+print('Finished!')
+
+"""
+에폭 1000, lr 0.001, 정확도 32.6~32.9% 
+다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9%
+"""
\ No newline at end of file

From 3a2f31c8b4f13933038d643301ad4c3131e46899 Mon Sep 17 00:00:00 2001
From: smallerhand 
Date: Fri, 27 Apr 2018 00:04:51 +0900
Subject: [PATCH 09/42] all

---
 x_ksh/sound_analysis3.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/x_ksh/sound_analysis3.py b/x_ksh/sound_analysis3.py
index 8dc34ad..8003515 100644
--- a/x_ksh/sound_analysis3.py
+++ b/x_ksh/sound_analysis3.py
@@ -63,8 +63,8 @@ def Labeling(label):
 testLabel=Labeling(testLabel)
 print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel))
 
-#다시 돌릴때는 여기부터 
-tf.reset_default_graph()     #그래프 초기화
+#start here
+tf.reset_default_graph()     #reset graph
 
 # hyper parameters
 learning_rate = 0.0001

From cd8f0c48be9263e77d2899dca1d3202618aeec0d Mon Sep 17 00:00:00 2001
From: smallerhand 
Date: Fri, 27 Apr 2018 00:21:47 +0900
Subject: [PATCH 10/42] all

---
 x_ksh/sound_analysis3.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/x_ksh/sound_analysis3.py b/x_ksh/sound_analysis3.py
index 8003515..0f12d78 100644
--- a/x_ksh/sound_analysis3.py
+++ b/x_ksh/sound_analysis3.py
@@ -15,7 +15,8 @@
 import tensorflow as tf
 tf.set_random_seed(777) 
 import pandas as pd
-train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv')
+#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv')
+train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv')
 
 #train/test, Data/Label split
 train_set, test_set = train_test_split(train, test_size = 0.3)
@@ -25,7 +26,7 @@
 testLabel = test_set.values[:,1]
 
 #data load and extract mfcc (scaling indluded)
-path = '/Users/kimseunghyuck/desktop/audio_train/'
+path = '/home/paperspace/Downloads/audio_train/
 
 def data2array(file):
     dic = {}

From 91dc56d3ff2d365a08d4d95f1b6875b4c9dbbdf8 Mon Sep 17 00:00:00 2001
From: smallerhand 
Date: Fri, 27 Apr 2018 00:24:29 +0900
Subject: [PATCH 11/42] all

---
 x_ksh/sound_analysis3.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/x_ksh/sound_analysis3.py b/x_ksh/sound_analysis3.py
index 0f12d78..1d3b5ab 100644
--- a/x_ksh/sound_analysis3.py
+++ b/x_ksh/sound_analysis3.py
@@ -26,7 +26,7 @@
 testLabel = test_set.values[:,1]
 
 #data load and extract mfcc (scaling indluded)
-path = '/home/paperspace/Downloads/audio_train/
+path = '/home/paperspace/Downloads/audio_train/'
 
 def data2array(file):
     dic = {}
@@ -121,7 +121,7 @@ def Labeling(label):
         accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
         print('Accuracy:', sess.run(accuracy, feed_dict={
                 X: testData, Y: testLabel.reshape(-1, 1), keep_prob: 1}))
-        save_path = saver.save(sess, '/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/optx/optx')
+        save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx')
 print('Finished!')
 
 """

From be584dc344123f5e971a3fd49c03bf7a829721b1 Mon Sep 17 00:00:00 2001
From: smallerhand 
Date: Fri, 27 Apr 2018 01:06:06 +0900
Subject: [PATCH 12/42] all

---
 x_ksh/optx/optx.data-00000-of-00001 | Bin 554996 -> 542708 bytes
 x_ksh/optx/optx.index               | Bin 695 -> 694 bytes
 x_ksh/optx/optx.meta                | Bin 227710 -> 72614 bytes
 x_ksh/sound_analysis3.py            |  13 ++++++++-----
 4 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/x_ksh/optx/optx.data-00000-of-00001 b/x_ksh/optx/optx.data-00000-of-00001
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32.6~32.9% 다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9% +위랑 같고 에폭 2300, 정확도 38.0~39.0% +lr을 더 줄이거나 늘리면 정확도가 확 낮아짐. """ \ No newline at end of file From 6d87e9b07d00e9319d463151633a4cb02cfc4068 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Fri, 27 Apr 2018 01:11:22 +0900 Subject: [PATCH 14/42] all --- x_ksh/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/x_ksh/README.md b/x_ksh/README.md index f426666..403203a 100644 --- a/x_ksh/README.md +++ b/x_ksh/README.md @@ -12,14 +12,12 @@ import tensorflow as tf 1. 데이터 전처리 1
    -
     y, sr = soundfile.read(path+filename, dtype='float32')
     mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
     max_mfcc=np.max(mfcc, axis=1)
     mins, maxs=np.min(max_mfcc), np.max(max_mfcc)
     scaled_mfcc=(max_mfcc-mins)/(maxs-mins)
     dic[i] = scaled_mfcc
    -
     soundfile.read 로 y, sr 추출
     librosa.feature.mfcc 로 mfcc 변환 (n_mfcc=20)
     20개 속성들 각각의 max값을 뽑음
    @@ -57,6 +55,9 @@ keep_prob = 1
     다른 조건 같음
     keep_prob = 0.8 (트레이닝때만)
     정확도 36.9~37.9%
    +최고 39.0%
    +
    +특이사항: lr을 저거보다 높게 하거나 낮게 하면 정확도가 확 떨어짐.
     
    From 7bbda4b652e61e40df37dbdee171a39923e7df36 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Fri, 27 Apr 2018 05:34:58 +0900 Subject: [PATCH 15/42] =?UTF-8?q?feature=20=EA=B0=92=EB=93=A4=EC=9D=84=20?= =?UTF-8?q?=EC=B6=94=EC=B6=9C=20+=20feature=EA=B0=92=EB=93=A4=EC=9D=84=20?= =?UTF-8?q?=EC=A0=95=EC=82=AC=EA=B0=81=ED=98=95=EC=9C=BC=EB=A1=9C=20?= =?UTF-8?q?=EB=B3=80=ED=99=98?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- x_ysw/feature2square.py | 122 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 x_ysw/feature2square.py diff --git a/x_ysw/feature2square.py b/x_ysw/feature2square.py new file mode 100644 index 0000000..da49745 --- /dev/null +++ b/x_ysw/feature2square.py @@ -0,0 +1,122 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Apr 27 04:28:33 2018 + +@author: modes +""" + +import os +import glob +import librosa +import numpy as np +import matplotlib.pyplot as plt + + +""" +특성추출 +stft(Short-time Fourier transform) :복소수 값을 갖는 행렬을 반환 +mfcc(Mel-frequency cepstral coefficients), +chroma_stft(chromagram from a waveform or power spectrogram), +melspectrogram(Mel-scaled power spectrogram), +spectral_contrast(spectral contrast), +tonnetz(tonal centroid features) +""" +def extract_feature(file_name): + X, sample_rate = librosa.load(file_name) #부동 소수점 시계열로 로드, sample_rate:자동 리샘플링(default sr=22050) + stft = np.abs(librosa.stft(X)) #stft를 절대값으로 + mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0) #오디오 신호를 mfcc로 바꿈 + chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0) # stft에서 chromagram 계산 + mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0) #melspectrogram + contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0) # spectral_contrast + tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0) #tonnetz + return mfccs,chroma,mel,contrast,tonnetz + +#hstack feature matrix +def parse_audio_files(filenames): + rows = len(filenames) + features = np.zeros((rows,193)) + i = 0 + for f_names in filenames: + mfccs, chroma, mel, contrast, tonnetz = extract_feature(f_names) + features[i] = np.hstack([mfccs, chroma, mel, contrast, tonnetz]) + i += 1 + return features + +#file_list 생성 +file = "C:/data/sound/audio_train/*.wav" +train_list=glob.glob(file) + +file = "c:/data/sound/audio_test/*.wav" +test_list=glob.glob(file) + +#extrect audio features +feature_train = parse_audio_files(train_list) +feature_test = parse_audio_files(test_list) + +np.savetxt("c:/data/sound/feature_train.csv",feature_train, delimiter=",") + + +#mel +l = train_list[300] +y,sr = librosa.load(l, duration=2.97) +mel = librosa.feature.melspectrogram(y=y, sr=sr).T #melspectrogram +#mel = np.mean(librosa.feature.melspectrogram(y=y, sr=sr).T,axis=0) +mel.shape #Out[21]: (105, 128) + +plt.subplots(1,1, figsize=(5,5)) +plt.imshow(mel) + +#mfcc +l = train_list[2000] +y,sr = librosa.load(l, duration=2.97) +mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40).T #melspectrogram +mfcc.shape #Out[21]: (105, 128) + +plt.subplots(1,1, figsize=(5,5)) +plt.imshow(mfcc) + + +#zero padding funtion +def mel2square(mel): +# 1 zero padded to the top +# 2 zeros padded to the bottom +# 2 zeros padded to the left +# 1 zero padded to the right + y,x=mel.shape + return np.pad(mel, ((0,abs(x-y)),(0,0)), 'constant') +mel.shape #Out[96]: (105, 128) +mel2square(mel).shape #Out[95]: (128, 128) + +plt.subplots(1,1, figsize=(5,5)) +plt.imshow(mel) + + +plt.subplots(1,1, figsize=(5,5)) +plt.imshow(mel2square(mel)) + + +def mfcc2square(mel): +# 1 zero padded to the top +# 2 zeros padded to the bottom +# 2 zeros padded to the left +# 1 zero padded to the right + y,x=mel.shape + return np.pad(mel, ((0,0),(0,abs(x-y))), 'constant') + +mfcc.shape #Out[135]: (128, 40) +mfcc2square(mfcc).shape #Out[136]: (128, 128) + +plt.subplots(1,1, figsize=(5,5)) +plt.imshow(mfcc) +plt.subplots(1,1, figsize=(5,5)) +plt.imshow(mfcc2square(mfcc)) + +#동시 2개 출력?????????????????? +fig1, axis =plt.subplots(1,2, figsize=(15,15)) +for i in range(2): + axis[i].imshow(mfcc) + axis[i].imshow(mfcc2square(mel)) + + + + From c7897a8a5bbdb07b631c42e0f90cdaf2098ddd86 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Fri, 27 Apr 2018 12:08:56 +0900 Subject: [PATCH 16/42] Add files via upload --- x_ysw/audio_labels.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 x_ysw/audio_labels.py diff --git a/x_ysw/audio_labels.py b/x_ysw/audio_labels.py new file mode 100644 index 0000000..965b55a --- /dev/null +++ b/x_ysw/audio_labels.py @@ -0,0 +1,21 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Apr 27 12:05:50 2018 + +@author: stu +""" +import numpy as np +import pandas as pd + +td = pd.read_csv("c:/data/sound/feature_train.csv",delimiter=',') +labels = train_info['label'] +l = train_info['label'].unique() + +df_label = pd.DataFrame(labels) + +for i in range(len(l)): + df_label[df_label==l[i]] = int(i) +df_label.values + +df_label.to_csv("c:/data/sound/audio_label.csv",index=False,mode='w') +td = pd.read_csv("c:/data/sound/audio_label.csv",delimiter=',') From eda63cea9c1bb88a20eb7e281e51a126b712ef80 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Fri, 27 Apr 2018 13:55:06 +0900 Subject: [PATCH 17/42] =?UTF-8?q?=EC=98=A4=EB=94=94=EC=98=A4=20=EB=9D=BC?= =?UTF-8?q?=EB=B2=A8=20=EB=B3=80=ED=98=95=20=EC=BD=94=EB=93=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit From 5dde33ff4a22bfb716d2751f537648e12a653f75 Mon Sep 17 00:00:00 2001 From: suwonyu <37686127+suwonyu@users.noreply.github.com> Date: Fri, 27 Apr 2018 13:56:50 +0900 Subject: [PATCH 18/42] Add files via upload --- PROJECT2/audio_label.csv | 9474 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 9474 insertions(+) create mode 100644 PROJECT2/audio_label.csv diff --git a/PROJECT2/audio_label.csv b/PROJECT2/audio_label.csv new file mode 100644 index 0000000..3827de8 --- /dev/null +++ b/PROJECT2/audio_label.csv @@ -0,0 +1,9474 @@ +label +0 +1 +2 +3 +4 +4 +5 +6 +7 +8 +0 +9 +10 +11 +4 +12 +12 +13 +14 +12 +15 +16 +0 +17 +18 +10 +16 +19 +3 +20 +21 +14 +4 +22 +23 +24 +25 +23 +7 +24 +7 +26 +11 +27 +28 +25 +4 +29 +29 +30 +0 +31 +0 +31 +19 +32 +30 +26 +25 +29 +18 +21 +11 +25 +30 +25 +13 +27 +6 +15 +19 +14 +9 +23 +28 +22 +5 +29 +8 +33 +27 +31 +12 +21 +32 +9 +0 +0 +5 +14 +33 +34 +7 +31 +35 +29 +4 +9 +6 +18 +16 +13 +11 +7 +17 +29 +4 +32 +25 +7 +16 +36 +37 +4 +31 +2 +9 +38 +32 +33 +30 +23 +20 +15 +5 +35 +3 +30 +33 +5 +29 +2 +34 +36 +15 +0 +21 +7 +26 +12 +39 +13 +31 +23 +2 +34 +10 +12 +2 +39 +15 +2 +19 +23 +26 +17 +26 +4 +37 +40 +11 +5 +19 +5 +10 +7 +28 +5 +30 +21 +39 +29 +40 +21 +5 +19 +14 +29 +31 +10 +37 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4a9397d1ec80b07571328717814cfd548eb7342d Mon Sep 17 00:00:00 2001 From: smallerhand Date: Fri, 27 Apr 2018 15:16:12 +0900 Subject: [PATCH 19/42] all --- README.md | 6 ++ x_ksh/sound_analysis4.py | 175 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 181 insertions(+) create mode 100644 x_ksh/sound_analysis4.py diff --git a/README.md b/README.md index 2b5d138..da00e5a 100644 --- a/README.md +++ b/README.md @@ -69,6 +69,12 @@ mfcc로 하면 41개 속성값이 나옴. 다른 특징 붙이니까 192개로 5월 4일 발표.
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    +## 추가 정보 +연철이가 test.csv 중에 안되는 파일 목록 알려줌(3개) +
    +0b0427e2, 6ea0099f, b39975f5
    +
    + ## 지난 주제: 1. MNIST DIGITS RECOGNIZER (2018. 4/9 ~ 4/20)
    diff --git a/x_ksh/sound_analysis4.py b/x_ksh/sound_analysis4.py
    new file mode 100644
    index 0000000..8ba4b50
    --- /dev/null
    +++ b/x_ksh/sound_analysis4.py
    @@ -0,0 +1,175 @@
    +#!/usr/bin/env python3
    +# -*- coding: utf-8 -*-
    +"""
    +Created on Thu Apr 26 18:53:10 2018
    +
    +@author: kimseunghyuck
    +"""
    +
    +import librosa
    +import soundfile as sf
    +from matplotlib import pyplot as plt
    +import numpy as np
    +import os
    +path = '/Users/kimseunghyuck/desktop/audio_train/'
    +files=os.listdir(path)
    +
    +#show one sample file
    +filename = files[0]
    +y, sr = sf.read(path+filename, dtype='float32')
    +#mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +stft=librosa.core.stft(y=y)
    +stft.shape  #1025, 161
    +
    +#show second sample file
    +filename = files[1]
    +y, sr = sf.read(path+filename, dtype='float32')
    +#mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +stft=librosa.core.stft(y=y)
    +stft.shape  #1025, 109
    +#주파수 범위가 1025, 109는 시간
    +#패딩해서 max로 맟춘 다음에 cnn하면 되지 않을까.
    +
    +#show graph
    +plt.figure(figsize=(15, 5))
    +plt.plot(stft)
    +
    +#show abs graph
    +stft=np.abs(stft)
    +plt.figure(figsize=(15, 5))
    +plt.plot(stft)
    +
    +#import labels
    +import tensorflow as tf
    +tf.set_random_seed(777) 
    +import pandas as pd
    +train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv')
    +
    +#train/test, Data/Label split
    +from sklearn.model_selection import train_test_split
    +train_set, test_set = train_test_split(train, test_size = 0.3)
    +trainfile = train_set.values[:,0]
    +testfile = test_set.values[:,0]
    +trainLabel = train_set.values[:,1]
    +testLabel = test_set.values[:,1]
    +
    +#data load and extract mfcc (scaling indluded)
    +path = '/Users/kimseunghyuck/desktop/audio_train/'
    +
    +def see_how_long(file):
    +    c=[]
    +    for filename in file:
    +        y, sr = sf.read(path+filename, dtype='float32')
    +        stft=librosa.core.stft(y=y)
    +        abs_stft=np.abs(stft)
    +        #1025 X t 형태
    +        c.append(abs_stft.shape[1])
    +    return(c)
    + 
    +n=see_how_long(trainfile)
    +print(np.max(n), np.min(n))      #2584 28
    +n2=see_how_long(testfile)
    +print(np.max(n2), np.min(n2))    #2584 26
    +
    +#zero padding to 3000
    +def data2array(file):
    +    #zero padding to file.shape[0] X 1025 X 3000
    +    k=0
    +    array = np.repeat(0, file.shape[0]*1025*3000).reshape(file.shape[0], 1025, 3000)
    +    for filename in file:
    +        y, sr = sf.read(path+filename, dtype='float32')
    +        stft=librosa.core.stft(y=y)
    +        abs_stft=np.abs(stft)
    +        for i in range(abs_stft.shape[0]):
    +            for j in range(abs_stft.shape[1]):
    +                array[k, i, j]=abs_stft[k, i, j]
    +        k+=1
    +    return(array)
    +
    +trainData=data2array(trainfile)
    +testData=data2array(testfile)
    +
    +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape)
    +# (6631, 20) (2842, 20) (6631,) (2842,)
    +
    +#how many kinds of label?
    +print(len(np.unique(trainLabel)))   #41
    +print(len(np.unique(testLabel)))    #41
    +
    +#label string -> integer(0~40)
    +idx = np.unique(trainLabel)
    +
    +def Labeling(label):
    +    r=pd.Series(label)
    +    for i in range(len(idx)):
    +        r[r.values==idx[i]]=i
    +    return(r)
    +
    +trainLabel=Labeling(trainLabel)
    +testLabel=Labeling(testLabel)
    +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel))
    +
    +#다시 돌릴때는 여기부터 
    +tf.reset_default_graph()     #그래프 초기화
    +
    +# hyper parameters
    +learning_rate = 0.0001
    +training_epochs = 5000
    +batch_size = 100
    +steps_for_validate = 5
    +
    +#placeholders
    +X = tf.placeholder(tf.float32, [None, 20], name="X") 
    +Y = tf.placeholder(tf.int32, [None, 1], name="Y")
    +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41])
    +keep_prob = tf.placeholder(tf.float32)
    +
    +# weights & bias for nn layers
    +W1 = tf.Variable(tf.random_normal([20, 128]))
    +b1 = tf.Variable(tf.random_normal([128]))
    +L1 = tf.nn.relu(tf.matmul(X, W1) + b1)
    +L1 = tf.nn.dropout(L1, keep_prob=keep_prob)
    +
    +W2 = tf.Variable(tf.random_normal([128, 256]))
    +b2 = tf.Variable(tf.random_normal([256]))
    +L2 = tf.nn.relu(tf.matmul(L1, W2) + b2)
    +L2 = tf.nn.dropout(L2, keep_prob=keep_prob)
    +
    +W4 = tf.Variable(tf.random_normal([256, 41]))
    +b4 = tf.Variable(tf.random_normal([41]))
    +hypothesis = tf.matmul(L2, W4) + b4
    +
    +# define cost/loss & optimizer
    +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    +    logits=hypothesis, labels=Y_onehot))
    +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
    +
    +# initialize
    +sess = tf.Session()
    +sess.run(tf.global_variables_initializer())
    +saver = tf.train.Saver()
    +
    +# train my model
    +print('Learning started. It takes sometime.')
    +for epoch in range(training_epochs):
    +    avg_cost = 0
    +    total_batch = int(len(trainData) / batch_size)
    +    for i in range(total_batch):
    +        batch_xs = trainData[i*batch_size:(i+1)*batch_size]
    +        batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1)
    +        feed_dict = {X: batch_xs, Y: batch_ys, keep_prob: .8}
    +        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
    +        avg_cost += c / total_batch
    +    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
    +    if epoch % steps_for_validate == steps_for_validate-1:
    +        correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y_onehot, 1))
    +        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    +        print('Accuracy:', sess.run(accuracy, feed_dict={
    +                X: testData, Y: testLabel.reshape(-1, 1), keep_prob: 1}))
    +        save_path = saver.save(sess, '/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/optx/optx')
    +print('Finished!')
    +
    +"""
    +에폭 1000, lr 0.001, 정확도 32.6~32.9% 
    +다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9%
    +"""
    \ No newline at end of file
    
    From c24f36fb534006d3d2722ec73f8847190a532249 Mon Sep 17 00:00:00 2001
    From: smallerhand 
    Date: Fri, 27 Apr 2018 16:46:25 +0900
    Subject: [PATCH 20/42] all
    
    ---
     x_ksh/sound_analysis4.py        |  17 ++-
     x_ksh/sound_analysis5.py        | 201 ++++++++++++++++++++++++++++++++
     x_ksh/sound_analysis5_cnn_ps.py | 174 +++++++++++++++++++++++++++
     3 files changed, 387 insertions(+), 5 deletions(-)
     create mode 100644 x_ksh/sound_analysis5.py
     create mode 100644 x_ksh/sound_analysis5_cnn_ps.py
    
    diff --git a/x_ksh/sound_analysis4.py b/x_ksh/sound_analysis4.py
    index 8ba4b50..8bf149d 100644
    --- a/x_ksh/sound_analysis4.py
    +++ b/x_ksh/sound_analysis4.py
    @@ -66,16 +66,19 @@ def see_how_long(file):
             c.append(abs_stft.shape[1])
         return(c)
      
    -n=see_how_long(trainfile)
    -print(np.max(n), np.min(n))      #2584 28
    -n2=see_how_long(testfile)
    -print(np.max(n2), np.min(n2))    #2584 26
    +#n=see_how_long(trainfile)
    +#print(np.max(n), np.min(n))      #2584 28
    +#n2=see_how_long(testfile)
    +#print(np.max(n2), np.min(n2))    #2584 26
    +file=trainfile
     
     #zero padding to 3000
     def data2array(file):
         #zero padding to file.shape[0] X 1025 X 3000
    +    n=file.shape[0]
         k=0
    -    array = np.repeat(0, file.shape[0]*1025*3000).reshape(file.shape[0], 1025, 3000)
    +    array = np.repeat(0, n * 1025 * 3000).reshape(n, 1025, 3000)
    +
         for filename in file:
             y, sr = sf.read(path+filename, dtype='float32')
             stft=librosa.core.stft(y=y)
    @@ -86,6 +89,10 @@ def data2array(file):
             k+=1
         return(array)
     
    +
    +import numpy as np
    +x=np.repeat(0, 6000*20*5000).reshape(6000,20,5000)
    +
     trainData=data2array(trainfile)
     testData=data2array(testfile)
     
    diff --git a/x_ksh/sound_analysis5.py b/x_ksh/sound_analysis5.py
    new file mode 100644
    index 0000000..bd88433
    --- /dev/null
    +++ b/x_ksh/sound_analysis5.py
    @@ -0,0 +1,201 @@
    +#!/usr/bin/env python3
    +# -*- coding: utf-8 -*-
    +"""
    +Created on Thu Apr 26 18:53:10 2018
    +
    +@author: kimseunghyuck
    +"""
    +
    +import librosa
    +import soundfile as sf
    +from matplotlib import pyplot as plt
    +import numpy as np
    +import os
    +path = '/Users/kimseunghyuck/desktop/audio_train/'
    +files=os.listdir(path)
    +
    +#show one sample file
    +filename = files[0]
    +y, sr = sf.read(path+filename, dtype='float32')
    +mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +#stft=librosa.core.stft(y=y)
    +#stft.shape  #1025, 161
    +mfcc.shape   #20, 161
    +
    +#show second sample file
    +filename = files[1]
    +y, sr = sf.read(path+filename, dtype='float32')
    +#mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +stft=librosa.core.stft(y=y)
    +stft.shape  #1025, 109
    +#주파수 범위가 1025, 109는 시간
    +#패딩해서 max로 맟춘 다음에 cnn하면 되지 않을까.
    +
    +#show graph
    +plt.figure(figsize=(15, 5))
    +plt.plot(mfcc)
    +
    +#show abs graph
    +mfcc=np.abs(mfcc)
    +plt.figure(figsize=(15, 5))
    +plt.plot(mfcc)
    +
    +#import labels
    +import tensorflow as tf
    +tf.set_random_seed(777) 
    +import pandas as pd
    +train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv')
    +
    +#train/test, Data/Label split
    +from sklearn.model_selection import train_test_split
    +train_set, test_set = train_test_split(train, test_size = 0.3)
    +trainfile = train_set.values[:,0]
    +testfile = test_set.values[:,0]
    +trainLabel = train_set.values[:,1]
    +testLabel = test_set.values[:,1]
    +
    +#data load and extract mfcc (scaling indluded)
    +path = '/Users/kimseunghyuck/desktop/audio_train/'
    +
    +def see_how_long(file):
    +    c=[]
    +    for filename in file:
    +        y, sr = sf.read(path+filename, dtype='float32')
    +        mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +        abs_mfcc=np.abs(mfcc)
    +        #1025 X t 형태
    +        c.append(abs_mfcc.shape[1])
    +    return(c)
    + 
    +#n=see_how_long(trainfile)
    +#print(np.max(n), np.min(n))      #2584 28
    +#n2=see_how_long(testfile)
    +#print(np.max(n2), np.min(n2))    #2584 26
    +
    +file=trainfile
    +file.shape
    +mfcc.shape
    +#zero padding to 3000
    +def data2array(file):
    +    #zero padding to file.shape[0] X 20 X 3000    
    +    n=file.shape[0]
    +    k=0
    +    array = np.repeat(0, n * 20 * 3000).reshape(n, 20, 3000)
    +    filename=file[0]
    +    for filename in file:
    +        y, sr = sf.read(path+filename, dtype='float32')
    +        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +        abs_mfcc=np.abs(mfcc)
    +        for j in range(abs_mfcc.shape[1]):
    +            array[k, :, j]=abs_mfcc[:, j]
    +        k+=1
    +    return(array)
    +
    +trainData=data2array(trainfile)
    +testData=data2array(testfile)
    +
    +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape)
    +# (6631, 20, 3000) (2842, 20, 3000) (6631,) (2842,)
    +
    +#how many kinds of label?
    +print(len(np.unique(trainLabel)))   #41
    +print(len(np.unique(testLabel)))    #41
    +
    +#label string -> integer(0~40)
    +idx = np.unique(trainLabel)
    +
    +def Labeling(label):
    +    r=pd.Series(label)
    +    for i in range(len(idx)):
    +        r[r.values==idx[i]]=i
    +    return(r)
    +
    +trainLabel=Labeling(trainLabel)
    +testLabel=Labeling(testLabel)
    +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel))
    +
    +#다시 돌릴때는 여기부터 
    +tf.reset_default_graph()     #그래프 초기화
    +
    +# hyper parameters
    +learning_rate = 0.001
    +training_epochs = 500
    +batch_size = 100
    +steps_for_validate = 5
    +
    +#placeholder
    +X = tf.placeholder(tf.float32, [None, 20, 3000], name="X")
    +X_sound = tf.reshape(X, [-1, 20, 3000, 1])          # 20*3000*1 (frequency, time, amplitude)
    +Y = tf.placeholder(tf.int32, [None, 1], name="Y")
    +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41])
    +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv")
    +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden")
    +
    +# L1 SoundIn shape=(?, 20, 3000, 1) 3000=20*150
    +W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer())
    +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME')
    +L1 = tf.nn.leaky_relu(L1,0.1)
    +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l1 shape=(?, 14, 14, 32)
    +L1 = tf.nn.dropout(L1, p_keep_conv)
    +
    +"""
    +# L2 SoundIn shape=(?, 10, 1500, 32)
    +W2 = tf.get_variable("W2", shape=[2, 15, 32, 64],initializer=tf.contrib.layers.xavier_initializer())
    +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
    +L2 = tf.nn.leaky_relu(L2,0.1)
    +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l2 shape=(?, 7, 7, 64)
    +L2 = tf.nn.dropout(L2, p_keep_conv)
    +
    +# L3 SoundIn shape=(?, 5, 750, 128)
    +W3 = tf.get_variable("W3", shape=[5, 75, 64, 128],initializer=tf.contrib.layers.xavier_initializer())
    +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME')
    +L3 = tf.nn.leaky_relu(L3,0.1)
    +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l3 shape=(?, 4, 4, 128)
    +L3 = tf.nn.dropout(L3, p_keep_conv)
    +L3_flat = tf.reshape(L3, shape=[-1, 128 * 75 * 64])    # reshape to (?, 2048)
    +"""
    +L1_flat= tf.reshape(L1, shape=[-1, 10*1500*32])
    +
    +# Final FC 4x4x128 inputs -> 10 outputs
    +W4 = tf.get_variable("W4", shape=[10*1500*32, 625],initializer=tf.contrib.layers.xavier_initializer())
    +L4 = tf.nn.leaky_relu(tf.matmul(L1_flat, W4),0.1)
    +L4 = tf.nn.dropout(L4, p_keep_hidden)
    +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer())
    +b = tf.Variable(tf.random_normal([41]))
    +logits = tf.matmul(L4, W_o) + b
    +
    +# define cost/loss & optimizer
    +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot))
    +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼
    +predict_op = tf.argmax(logits, 1, name="pred")
    +
    +# initialize
    +sess = tf.Session()
    +sess.run(tf.global_variables_initializer())
    +saver = tf.train.Saver()
    +
    +# train my model
    +print('Learning started. It takes sometime.')
    +for epoch in range(training_epochs):
    +    avg_cost = 0
    +    total_batch = int(len(trainData) / batch_size)
    +    for i in range(total_batch):
    +        batch_xs = trainData[i*batch_size:(i+1)*batch_size]
    +        batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1)
    +        feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5}
    +        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
    +        avg_cost += c / total_batch
    +    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
    +    if epoch % steps_for_validate == steps_for_validate-1:
    +        correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1))
    +        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    +        print('Accuracy:', sess.run(accuracy, feed_dict={
    +                X: testData, Y: testLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1}))
    +        save_path = saver.save(sess, '/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/optx/optx')
    +print('Finished!')
    +
    +
    +"""
    +에폭 1000, lr 0.001, 정확도 32.6~32.9% 
    +다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9%
    +"""
    \ No newline at end of file
    diff --git a/x_ksh/sound_analysis5_cnn_ps.py b/x_ksh/sound_analysis5_cnn_ps.py
    new file mode 100644
    index 0000000..5024893
    --- /dev/null
    +++ b/x_ksh/sound_analysis5_cnn_ps.py
    @@ -0,0 +1,174 @@
    +#!/usr/bin/env python3
    +# -*- coding: utf-8 -*-
    +"""
    +Created on Thu Apr 26 18:53:10 2018
    +
    +@author: kimseunghyuck
    +"""
    +
    +import librosa
    +import soundfile as sf
    +from matplotlib import pyplot as plt
    +import numpy as np
    +
    +#import labels
    +import tensorflow as tf
    +tf.set_random_seed(777) 
    +import pandas as pd
    +#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv')
    +train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv')
    +
    +#train/test, Data/Label split
    +from sklearn.model_selection import train_test_split
    +train_set, test_set = train_test_split(train, test_size = 0.3)
    +trainfile = train_set.values[:,0]
    +testfile = test_set.values[:,0]
    +trainLabel = train_set.values[:,1]
    +testLabel = test_set.values[:,1]
    +
    +#data load and extract mfcc (scaling indluded)
    +#path = '/Users/kimseunghyuck/desktop/audio_train/'
    +path = '/home/paperspace/Downloads/audio_train/'
    +
    +def see_how_long(file):
    +    c=[]
    +    for filename in file:
    +        y, sr = sf.read(path+filename, dtype='float32')
    +        mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +        abs_mfcc=np.abs(mfcc)
    +        #1025 X t 형태
    +        c.append(abs_mfcc.shape[1])
    +    return(c)
    + 
    +#n=see_how_long(trainfile)
    +#print(np.max(n), np.min(n))      #2584 28
    +#n2=see_how_long(testfile)
    +#print(np.max(n2), np.min(n2))    #2584 26
    +
    +file=trainfile
    +file.shape
    +mfcc.shape
    +#zero padding to 3000
    +def data2array(file):
    +    #zero padding to file.shape[0] X 20 X 3000    
    +    n=file.shape[0]
    +    k=0
    +    array = np.repeat(0, n * 20 * 3000).reshape(n, 20, 3000)
    +    filename=file[0]
    +    for filename in file:
    +        y, sr = sf.read(path+filename, dtype='float32')
    +        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    +        abs_mfcc=np.abs(mfcc)
    +        for j in range(abs_mfcc.shape[1]):
    +            array[k, :, j]=abs_mfcc[:, j]
    +        k+=1
    +    return(array)
    +
    +trainData=data2array(trainfile)
    +testData=data2array(testfile)
    +
    +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape)
    +# (6631, 20, 3000) (2842, 20, 3000) (6631,) (2842,)
    +
    +#how many kinds of label?
    +print(len(np.unique(trainLabel)))   #41
    +print(len(np.unique(testLabel)))    #41
    +
    +#label string -> integer(0~40)
    +idx = np.unique(trainLabel)
    +
    +def Labeling(label):
    +    r=pd.Series(label)
    +    for i in range(len(idx)):
    +        r[r.values==idx[i]]=i
    +    return(r)
    +
    +trainLabel=Labeling(trainLabel)
    +testLabel=Labeling(testLabel)
    +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel))
    +
    +#다시 돌릴때는 여기부터 
    +tf.reset_default_graph()     #그래프 초기화
    +
    +# hyper parameters
    +learning_rate = 0.001
    +training_epochs = 500
    +batch_size = 100
    +steps_for_validate = 5
    +
    +#placeholder
    +X = tf.placeholder(tf.float32, [None, 20, 3000], name="X")
    +X_sound = tf.reshape(X, [-1, 20, 3000, 1])          # 20*3000*1 (frequency, time, amplitude)
    +Y = tf.placeholder(tf.int32, [None, 1], name="Y")
    +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41])
    +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv")
    +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden")
    +
    +# L1 SoundIn shape=(?, 20, 3000, 1) 3000=20*150
    +W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer())
    +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME')
    +L1 = tf.nn.leaky_relu(L1,0.1)
    +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l1 shape=(?, 14, 14, 32)
    +L1 = tf.nn.dropout(L1, p_keep_conv)
    +
    +"""
    +# L2 SoundIn shape=(?, 10, 1500, 32)
    +W2 = tf.get_variable("W2", shape=[2, 15, 32, 64],initializer=tf.contrib.layers.xavier_initializer())
    +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
    +L2 = tf.nn.leaky_relu(L2,0.1)
    +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l2 shape=(?, 7, 7, 64)
    +L2 = tf.nn.dropout(L2, p_keep_conv)
    +
    +# L3 SoundIn shape=(?, 5, 750, 128)
    +W3 = tf.get_variable("W3", shape=[5, 75, 64, 128],initializer=tf.contrib.layers.xavier_initializer())
    +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME')
    +L3 = tf.nn.leaky_relu(L3,0.1)
    +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l3 shape=(?, 4, 4, 128)
    +L3 = tf.nn.dropout(L3, p_keep_conv)
    +L3_flat = tf.reshape(L3, shape=[-1, 128 * 75 * 64])    # reshape to (?, 2048)
    +"""
    +L1_flat= tf.reshape(L1, shape=[-1, 10*1500*32])
    +
    +# Final FC 4x4x128 inputs -> 10 outputs
    +W4 = tf.get_variable("W4", shape=[10*1500*32, 625],initializer=tf.contrib.layers.xavier_initializer())
    +L4 = tf.nn.leaky_relu(tf.matmul(L1_flat, W4),0.1)
    +L4 = tf.nn.dropout(L4, p_keep_hidden)
    +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer())
    +b = tf.Variable(tf.random_normal([41]))
    +logits = tf.matmul(L4, W_o) + b
    +
    +# define cost/loss & optimizer
    +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot))
    +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼
    +predict_op = tf.argmax(logits, 1, name="pred")
    +
    +# initialize
    +sess = tf.Session()
    +sess.run(tf.global_variables_initializer())
    +saver = tf.train.Saver()
    +
    +# train my model
    +print('Learning started. It takes sometime.')
    +for epoch in range(training_epochs):
    +    avg_cost = 0
    +    total_batch = int(len(trainData) / batch_size)
    +    for i in range(total_batch):
    +        batch_xs = trainData[i*batch_size:(i+1)*batch_size]
    +        batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1)
    +        feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5}
    +        c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
    +        avg_cost += c / total_batch
    +    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
    +    if epoch % steps_for_validate == steps_for_validate-1:
    +        correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1))
    +        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    +        print('Accuracy:', sess.run(accuracy, feed_dict={
    +                X: testData, Y: testLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1}))
    +        save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx')
    +print('Finished!')
    +
    +
    +"""
    +에폭 1000, lr 0.001, 정확도 32.6~32.9% 
    +다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9%
    +"""
    \ No newline at end of file
    
    From 279a426a982283c2f51fbe5458cea22f266a084e Mon Sep 17 00:00:00 2001
    From: smallerhand 
    Date: Fri, 27 Apr 2018 16:57:00 +0900
    Subject: [PATCH 21/42] all
    
    ---
     x_ksh/sound_analysis5_cnn_ps.py | 4 ----
     1 file changed, 4 deletions(-)
    
    diff --git a/x_ksh/sound_analysis5_cnn_ps.py b/x_ksh/sound_analysis5_cnn_ps.py
    index 5024893..7f31fbf 100644
    --- a/x_ksh/sound_analysis5_cnn_ps.py
    +++ b/x_ksh/sound_analysis5_cnn_ps.py
    @@ -8,7 +8,6 @@
     
     import librosa
     import soundfile as sf
    -from matplotlib import pyplot as plt
     import numpy as np
     
     #import labels
    @@ -45,9 +44,6 @@ def see_how_long(file):
     #n2=see_how_long(testfile)
     #print(np.max(n2), np.min(n2))    #2584 26
     
    -file=trainfile
    -file.shape
    -mfcc.shape
     #zero padding to 3000
     def data2array(file):
         #zero padding to file.shape[0] X 20 X 3000    
    
    From 9944e6efbfc32556866130db5d58be7ea6e091c8 Mon Sep 17 00:00:00 2001
    From: smallerhand 
    Date: Fri, 27 Apr 2018 17:08:27 +0900
    Subject: [PATCH 22/42] all
    
    ---
     x_ksh/sound_analysis5_cnn_ps.py | 8 ++++----
     1 file changed, 4 insertions(+), 4 deletions(-)
    
    diff --git a/x_ksh/sound_analysis5_cnn_ps.py b/x_ksh/sound_analysis5_cnn_ps.py
    index 7f31fbf..10c1f09 100644
    --- a/x_ksh/sound_analysis5_cnn_ps.py
    +++ b/x_ksh/sound_analysis5_cnn_ps.py
    @@ -103,7 +103,7 @@ def Labeling(label):
     # L1 SoundIn shape=(?, 20, 3000, 1) 3000=20*150
     W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer())
     L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME')
    -L1 = tf.nn.leaky_relu(L1,0.1)
    +L1 = tf.nn.elu(L1)
     L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l1 shape=(?, 14, 14, 32)
     L1 = tf.nn.dropout(L1, p_keep_conv)
     
    @@ -111,14 +111,14 @@ def Labeling(label):
     # L2 SoundIn shape=(?, 10, 1500, 32)
     W2 = tf.get_variable("W2", shape=[2, 15, 32, 64],initializer=tf.contrib.layers.xavier_initializer())
     L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
    -L2 = tf.nn.leaky_relu(L2,0.1)
    +L2 = tf.nn.elu(L2)
     L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l2 shape=(?, 7, 7, 64)
     L2 = tf.nn.dropout(L2, p_keep_conv)
     
     # L3 SoundIn shape=(?, 5, 750, 128)
     W3 = tf.get_variable("W3", shape=[5, 75, 64, 128],initializer=tf.contrib.layers.xavier_initializer())
     L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME')
    -L3 = tf.nn.leaky_relu(L3,0.1)
    +L3 = tf.nn.elu(L3)
     L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l3 shape=(?, 4, 4, 128)
     L3 = tf.nn.dropout(L3, p_keep_conv)
     L3_flat = tf.reshape(L3, shape=[-1, 128 * 75 * 64])    # reshape to (?, 2048)
    @@ -127,7 +127,7 @@ def Labeling(label):
     
     # Final FC 4x4x128 inputs -> 10 outputs
     W4 = tf.get_variable("W4", shape=[10*1500*32, 625],initializer=tf.contrib.layers.xavier_initializer())
    -L4 = tf.nn.leaky_relu(tf.matmul(L1_flat, W4),0.1)
    +L4 = tf.nn.elu(tf.matmul(L1_flat, W4))
     L4 = tf.nn.dropout(L4, p_keep_hidden)
     W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer())
     b = tf.Variable(tf.random_normal([41]))
    
    From b8578f3aabd85934b7342916b0bd4f9c664c138d Mon Sep 17 00:00:00 2001
    From: smallerhand 
    Date: Fri, 27 Apr 2018 17:34:52 +0900
    Subject: [PATCH 23/42] all
    
    ---
     x_ksh/sound_analysis5_cnn_ps.py | 23 +++--------------------
     1 file changed, 3 insertions(+), 20 deletions(-)
    
    diff --git a/x_ksh/sound_analysis5_cnn_ps.py b/x_ksh/sound_analysis5_cnn_ps.py
    index 10c1f09..667cb8f 100644
    --- a/x_ksh/sound_analysis5_cnn_ps.py
    +++ b/x_ksh/sound_analysis5_cnn_ps.py
    @@ -104,29 +104,12 @@ def Labeling(label):
     W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer())
     L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME')
     L1 = tf.nn.elu(L1)
    -L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l1 shape=(?, 14, 14, 32)
    +L1 = tf.nn.max_pool(L1, ksize=[1, 20, 20, 1],strides=[1, 20, 20, 1], padding='SAME') # l1 shape=(?, 14, 14, 32)
     L1 = tf.nn.dropout(L1, p_keep_conv)
    -
    -"""
    -# L2 SoundIn shape=(?, 10, 1500, 32)
    -W2 = tf.get_variable("W2", shape=[2, 15, 32, 64],initializer=tf.contrib.layers.xavier_initializer())
    -L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME')
    -L2 = tf.nn.elu(L2)
    -L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l2 shape=(?, 7, 7, 64)
    -L2 = tf.nn.dropout(L2, p_keep_conv)
    -
    -# L3 SoundIn shape=(?, 5, 750, 128)
    -W3 = tf.get_variable("W3", shape=[5, 75, 64, 128],initializer=tf.contrib.layers.xavier_initializer())
    -L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME')
    -L3 = tf.nn.elu(L3)
    -L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l3 shape=(?, 4, 4, 128)
    -L3 = tf.nn.dropout(L3, p_keep_conv)
    -L3_flat = tf.reshape(L3, shape=[-1, 128 * 75 * 64])    # reshape to (?, 2048)
    -"""
    -L1_flat= tf.reshape(L1, shape=[-1, 10*1500*32])
    +L1_flat= tf.reshape(L1, shape=[-1, 150*32])
     
     # Final FC 4x4x128 inputs -> 10 outputs
    -W4 = tf.get_variable("W4", shape=[10*1500*32, 625],initializer=tf.contrib.layers.xavier_initializer())
    +W4 = tf.get_variable("W4", shape=[150*32, 625],initializer=tf.contrib.layers.xavier_initializer())
     L4 = tf.nn.elu(tf.matmul(L1_flat, W4))
     L4 = tf.nn.dropout(L4, p_keep_hidden)
     W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer())
    
    From b590597a902397d7b8d4c06742fdf04b977b44c6 Mon Sep 17 00:00:00 2001
    From: smallerhand 
    Date: Fri, 27 Apr 2018 18:17:37 +0900
    Subject: [PATCH 24/42] all
    
    ---
     x_ksh/sound_analysis5_cnn_ps.py | 4 ++--
     1 file changed, 2 insertions(+), 2 deletions(-)
    
    diff --git a/x_ksh/sound_analysis5_cnn_ps.py b/x_ksh/sound_analysis5_cnn_ps.py
    index 667cb8f..db2896c 100644
    --- a/x_ksh/sound_analysis5_cnn_ps.py
    +++ b/x_ksh/sound_analysis5_cnn_ps.py
    @@ -142,11 +142,11 @@ def Labeling(label):
             correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1))
             accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
             print('Accuracy:', sess.run(accuracy, feed_dict={
    -                X: testData, Y: testLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1}))
    +                X: testData[:100], Y: testLabel[:100].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1}))
             save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx')
     print('Finished!')
     
    -
    +#테스트 할 때 샘플 추출 하는 방법 바꾸기
     """
     에폭 1000, lr 0.001, 정확도 32.6~32.9% 
     다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9%
    
    From da063a0c675699ab96c9467d3757800527ec131b Mon Sep 17 00:00:00 2001
    From: 1eedaegon 
    Date: Fri, 27 Apr 2018 18:37:37 +0900
    Subject: [PATCH 25/42] all
    
    ---
     x_ldg/Research/MFCC.md | 12 ++++++++++++
     1 file changed, 12 insertions(+)
     create mode 100644 x_ldg/Research/MFCC.md
    
    diff --git a/x_ldg/Research/MFCC.md b/x_ldg/Research/MFCC.md
    new file mode 100644
    index 0000000..4b8cfbf
    --- /dev/null
    +++ b/x_ldg/Research/MFCC.md
    @@ -0,0 +1,12 @@
    +## 1eedaegon - 이대곤(KYLius-method)
    +
    +### MFCC(Mel-frequency ceptral coefficients)가 무엇일까?
    +- 단계
    +
    +1. 시간별 소리신호를 작은 크기의 프레임으로 자른다. sampling
    +2. 각 프레임에 대해서 FTT적용, 주파수 계산 
    +3. 사람이 인식하는 소리영역으로 자름(15~15000hz), 주파수에서 mel scale로 간격나눔, 반대 공식도 있다.
    +4. 로그함수취함(값이 클수록 사람이 듣는 소리가 비례해서 커지는건 아님)
    +5. DCT == 필터뱅크 각각의 소리에 대해 행렬로 바꾼다.
    +6. 가장 특징이 큰 소리로 합침
    +
    From c0e20ec5fd6592101adaafa6fe92455f49dc9bcb Mon Sep 17 00:00:00 2001 From: smallerhand Date: Fri, 27 Apr 2018 19:57:47 +0900 Subject: [PATCH 26/42] all --- README.md | 27 +++++++++++++++++++-------- x_ksh/sound_analysis5_cnn_ps.py | 4 ++++ 2 files changed, 23 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index da00e5a..2ef83cc 100644 --- a/README.md +++ b/README.md @@ -52,19 +52,30 @@ mfcc로 하면 41개 속성값이 나옴. 다른 특징 붙이니까 192개로 승혁: librosa feature 뽑아내봄
    +## 할거 +1. 각자 만들고 있는 모델 완성하기
    -할거:
    -1. 음향학 공부(파이썬 함수 각각의 feature 중 뭘 써야 할지, 왜 쓰는지) + 전처리를 어떻게 할지
    -- 대곤(librosa, mfcc) <- librosa로 뽑아낼 수 있는 특징값 중에 mfcc는 다 뽑히는데 테스트셋에서 mfcc외의 특징은 잘 안뽑히는데 알아봐주세요.(수원 요청)
    -2. conv1d - 승혁, 상욱
    -3. conv2d - 수원, 상욱
    +각자 전부(승혁, 상욱, 대곤, 수원)
    +일요일 모이기 전까지 완성된 코드 올리기
    +코드+csv파일+opt올리기
    +(정확도 좋든 안좋든)
     
    +2. 용어, 함수(메소드), 알고리즘 나눠서 공부 +
    +stft(Short-time Fourier transform) :복소수 값을 갖는 행렬을 반환 - 승혁
    +- core.stft
    +- freature.chroma.stft
    +mfcc(Mel-frequency cepstral coefficients), - 상욱
    +melspectrogram(Mel-scaled power spectrogram), - 수원
    +spectral_contrast(spectral contrast), - 수원
    +tonnetz(tonal centroid features) - 대곤
    +
    + + ## 스케쥴표
    -25~27일 금요일 맡은거 해보고 오기.
    -27일 금요일 5시 회의.
    -29일 일요일 오후 회의.
    +29일 일요일 오후 2시 사당역.
     5월 2일부터 피피티.
     5월 4일 발표.
     
    diff --git a/x_ksh/sound_analysis5_cnn_ps.py b/x_ksh/sound_analysis5_cnn_ps.py index db2896c..1d936b3 100644 --- a/x_ksh/sound_analysis5_cnn_ps.py +++ b/x_ksh/sound_analysis5_cnn_ps.py @@ -146,6 +146,10 @@ def Labeling(label): save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') print('Finished!') +A=np.array([1,2,3,4,9,1,2]) + + + #테스트 할 때 샘플 추출 하는 방법 바꾸기 """ 에폭 1000, lr 0.001, 정확도 32.6~32.9% From 1fc453c434c56b317a0a6a2d5e5ca16b8c0c317f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=EA=B3=BD=EC=83=81=EC=9A=B1?= <37833373+Trigger21@users.noreply.github.com> Date: Fri, 27 Apr 2018 20:06:51 +0900 Subject: [PATCH 27/42] Create test.py --- x_ksw/New Project/test.py | 316 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 316 insertions(+) create mode 100644 x_ksw/New Project/test.py diff --git a/x_ksw/New Project/test.py b/x_ksw/New Project/test.py new file mode 100644 index 0000000..1fb9cf8 --- /dev/null +++ b/x_ksw/New Project/test.py @@ -0,0 +1,316 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Apr 23 23:59:42 2018 + +@author: modes +""" +import os +import glob +import librosa +import numpy as np +import pandas as pd +28*28 + +""" +특성추출 +stft(Short-time Fourier transform) :복소수 값을 갖는 행렬을 반환 +mfcc(Mel-frequency cepstral coefficients), +chroma_stft(chromagram from a waveform or power spectrogram), +melspectrogram(Mel-scaled power spectrogram), +spectral_contrast(spectral contrast), +tonnetz(tonal centroid features) +""" +def extract_feature(file_name): + X, sample_rate = librosa.load(file_name) #부동 소수점 시계열로 로드, sample_rate:자동 리샘플링(default sr=22050) + #stft = np.abs(librosa.stft(X)) #stft를 절대값으로 + mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=784).T,axis=0) #오디오 신호를 mfcc로 바꿈 + #chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0) # stft에서 chromagram 계산 + #mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0) #melspectrogram + #contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0) # spectral_contrast + #tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0) #tonnetz + #return mfccs,chroma,mel,contrast,tonnetz + return mfccs +#hstack feature matrix +def parse_audio_files(filenames): + rows = len(filenames) + features = np.zeros((rows,784)) + i = 0 + for f_names in filenames: + mfccs = extract_feature(f_names) + features[i] = np.hstack(mfccs) + i += 1 + return features + +#file_list 생성 +file = "/home/itwill03/sound/audio_train/*.wav" +train_list=glob.glob(file) + +file = "/home/itwill03/sound/audio_test/*.wav" +test_list=glob.glob(file) +test_list[1414] +len(train_list) + +#extrect audio features +feature_train = parse_audio_files(train_list) +feature_test = parse_audio_files(test_list) + +sf = "/home/itwill03/sound/audio_train/0a0a8d4c.wav" +sf = train_list[4100] +X, sample_rate = librosa.load(sf,sr=22050) +mfccs = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=128).T +plt.subplots(1,1, figsize=(5,5)) +plt.imshow(mfccs) + +feature_train = extract_feature(train_list) +feature_test = extract_feature(test_list) + +np.savetxt("/home/itwill03/sound/feature_train.csv",feature_train, delimiter=",") + + +len(feature_train) +len(train_list) + +feature_train.shape +feature_test.shape + +train = pd.read_csv("/home/itwill03/sound/train.csv") +ft = pd.read_csv("/home/itwill03/sound/feature_train.csv", header = None) +ft2 = pd.DataFrame(feature_train) +ft.loc[0] + +df = pd.concat([ft, train['label']], axis=1) + +train.head() +print("Number of training examples=", train.shape[0], " Number of classes=", len(train.label.unique())) +print(train.label.unique()) + +for i in range(len(feature_train)+1): + train['mfcc'][i] = feature_train[i] + +feature_train[0] + +train['fname'][0] +train['label'] +a = extract_feature('/home/itwill03/sound/audio_test/87f52da2.wav') +b = extract_feature('/home/itwill03/sound/audio_test/1819a7b7.wav') + + +len(a[0]) +len(b[1]) + +feature_train = np.zeros((len(train_list),40)) +i = 0 +for f_name in train_list: + feature_train[i] = extract_feature(f_name) + i += 1 +feature_train + + +40, + + +df +df['label'][0] +print(train.label.unique()) + +if df['label'][0] == 'Hi-hat': + df['lb'] = 0 + +for i in range(len(df)): + if df['label'][i] == 'Hi-hat': + df['lb'][i] = 0 + elif df['label'][i] == 'Saxophone': + df['lb'][i] = 1 + elif df['label'][i] == 'Trumpet': + df['lb'][i] = 2 + elif df['label'][i] == 'Glockenspiel': + df['lb'][i] = 3 + elif df['label'][i] == 'Cello': + df['lb'][i] = 4 + elif df['label'][i] == 'Knock': + df['lb'][i] = 5 + elif df['label'][i] == 'Gunshot_or_gunfire': + df['lb'][i] = 6 + elif df['label'][i] == 'Clarinet': + df['lb'][i] = 7 + elif df['label'][i] == 'Computer_keyboard': + df['lb'][i] = 8 + elif df['label'][i] == 'Keys_jangling': + df['lb'][i] = 9 + elif df['label'][i] == 'Snare_drum': + df['lb'][i] = 10 + elif df['label'][i] == 'Writing': + df['lb'][i] = 11 + elif df['label'][i] == 'Laughter': + df['lb'][i] = 12 + elif df['label'][i] == 'Tearing': + df['lb'][i] = 13 + elif df['label'][i] == 'Fart': + df['lb'][i] = 14 + elif df['label'][i] == 'Oboe': + df['lb'][i] = 15 + elif df['label'][i] == 'Flute': + df['lb'][i] = 16 + elif df['label'][i] == 'Cough': + df['lb'][i] = 17 + elif df['label'][i] == 'Telephone': + df['lb'][i] = 18 + elif df['label'][i] == 'Bark': + df['lb'][i] = 19 + elif df['label'][i] == 'Chime': + df['lb'][i] = 20 + elif df['label'][i] == 'Bass_drum': + df['lb'][i] = 21 + elif df['label'][i] == 'Bus': + df['lb'][i] = 22 + elif df['label'][i] == 'Squeak': + df['lb'][i] = 23 + elif df['label'][i] == 'Scissors': + df['lb'][i] = 24 + elif df['label'][i] == 'Harmonica': + df['lb'][i] = 25 + elif df['label'][i] == 'Gong': + df['lb'][i] = 26 + elif df['label'][i] == 'Microwave_oven': + df['lb'][i] = 27 + elif df['label'][i] == 'Burping_or_eructation': + df['lb'][i] = 28 + elif df['label'][i] == 'Double_bass': + df['lb'][i] = 29 + elif df['label'][i] == 'Shatter': + df['lb'][i] = 30 + elif df['label'][i] == 'Fireworks': + df['lb'][i] = 31 + elif df['label'][i] == 'Tambourine': + df['lb'][i] = 32 + elif df['label'][i] == 'Cowbell': + df['lb'][i] = 33 + elif df['label'][i] == 'Electric_piano': + df['lb'][i] = 34 + elif df['label'][i] == 'Meow': + df['lb'][i] = 35 + elif df['label'][i] == 'Drawer_open_or_close': + df['lb'][i] = 36 + elif df['label'][i] == 'Applause': + df['lb'][i] = 37 + elif df['label'][i] == 'Acoustic_guitar': + df['lb'][i] = 38 + elif df['label'][i] == 'Violin_or_fiddle': + df['lb'][i] = 39 + elif df['label'][i] == 'Finger_snapping': + df['lb'][i] = 40 + +np.savetxt("/home/itwill03/sound/df_train.csv",df, delimiter=",") + + +import tensorflow as tf +import numpy as np +tf.reset_default_graph() #그래프 초기화 +tf.set_random_seed(777) +import pandas as pd + +#train = pd.read_csv('c:/python/train.csv') +train = pd.read_csv('/home/itwill03/다운로드/train.csv') + +#훈련세트, validation세트 나누기 +from sklearn.model_selection import train_test_split +train_set, validate_set = train_test_split(df, test_size = 0.3) +trainData = train_set.values[:,:-1] +validateData = validate_set.values[:,:-1] +trainLabel=train_set.values[:,-1] +validateLabel=validate_set.values[:,-1] + +X = tf.placeholder(tf.float32, [None, 784]) +X_img = tf.reshape(X, [-1, 28, 28, 1]) # img 28x28x1 (black/white) +Y = tf.placeholder(tf.int32, [None, 1]) +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32) +p_keep_hidden = tf.placeholder(tf.float32) + +# hyper parameters +learning_rate = 0.00008 +training_epochs = 300 +batch_size = 100 +steps_for_validate = 5 +keep_prob = tf.placeholder(tf.float32) + +# L1 ImgIn shape=(?, 28, 28, 1) +#W1 = tf.Variable(tf.random_normal([3, 3, 1, 32], stddev=0.01)) +W1 = tf.get_variable("W1", shape=[3, 3, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_img, W1, strides=[1, 1, 1, 1], padding='SAME') +#L1 = tf.nn.relu(L1) +#L1 = tf.nn.elu(L1) +L1 = tf.nn.leaky_relu(L1,0.1) +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l1 shape=(?, 14, 14, 32) +L1 = tf.nn.dropout(L1, p_keep_conv) + +# L2 ImgIn shape=(?, 14, 14, 10) +#W2 = tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.01)) +W2 = tf.get_variable("W2", shape=[3, 3, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +#L2 = tf.nn.relu(L2) +#L2 = tf.nn.elu(L2) +L2 = tf.nn.leaky_relu(L2,0.1) +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l2 shape=(?, 7, 7, 64) +L2 = tf.nn.dropout(L2, p_keep_conv) + +# L3 ImgIn shape=(?, 7, 7, 128) +#W3 = tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.01)) +W3 = tf.get_variable("W3", shape=[3, 3, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') +#L3 = tf.nn.relu(L3) +#L3 = tf.nn.elu(L3) +L3 = tf.nn.leaky_relu(L3,0.1) +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') # l3 shape=(?, 4, 4, 128) +L3 = tf.nn.dropout(L3, p_keep_conv) +L3_flat = tf.reshape(L3, shape=[-1, 128 * 4 * 4]) # reshape to (?, 2048) + + +# Final FC 4x4x128 inputs -> 10 outputs +#W4 = tf.Variable(tf.random_normal([128 * 4 * 4, 625], stddev=0.01)) +W4 = tf.get_variable("W4", shape=[128 * 4 * 4, 625],initializer=tf.contrib.layers.xavier_initializer()) +#L4 = tf.nn.relu(tf.matmul(L3_flat, W4)) +#L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) +L4 = tf.nn.leaky_relu(tf.matmul(L3_flat, W4),0.1) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[625,10],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([10])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +#optimizer = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1) + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: validateData, Y: validateLabel.reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + saver.save(sess, '/home/itwill03/다운로드/cnn_session/cnn_session') + save_path = saver.save(sess, "/home/itwill03/다운로드/opt2/opt2") +print('Finished!') + +#saver.save(sess, 'cnn_session') +#save_path = saver.save(sess, "c:/Users/STU/opt2") + + +sess.close() From 0adeccf81c42e4e35f25288e782ab47485f59f27 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Fri, 27 Apr 2018 20:52:08 +0900 Subject: [PATCH 28/42] all --- x_ksh/SA_cnn_ps2.py | 168 +++++++++++++++++++++++++++++++ x_ksh/SA_cnn_ps3.py | 172 ++++++++++++++++++++++++++++++++ x_ksh/sound_analysis5_cnn_ps.py | 11 ++ 3 files changed, 351 insertions(+) create mode 100644 x_ksh/SA_cnn_ps2.py create mode 100644 x_ksh/SA_cnn_ps3.py diff --git a/x_ksh/SA_cnn_ps2.py b/x_ksh/SA_cnn_ps2.py new file mode 100644 index 0000000..a378de6 --- /dev/null +++ b/x_ksh/SA_cnn_ps2.py @@ -0,0 +1,168 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +import librosa +import soundfile as sf +import numpy as np + +#import labels +import tensorflow as tf +tf.set_random_seed(777) +import pandas as pd +#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') +train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') + +#train/test, Data/Label split +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(train, test_size = 0.3) +trainfile = train_set.values[:,0] +testfile = test_set.values[:,0] +trainLabel = train_set.values[:,1] +testLabel = test_set.values[:,1] + +#data load and extract mfcc (scaling indluded) +#path = '/Users/kimseunghyuck/desktop/audio_train/' +path = '/home/paperspace/Downloads/audio_train/' + +def see_how_long(file): + c=[] + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + #1025 X t 형태 + c.append(abs_mfcc.shape[1]) + return(c) + +#n=see_how_long(trainfile) +#print(np.max(n), np.min(n)) #2584 28 +#n2=see_how_long(testfile) +#print(np.max(n2), np.min(n2)) #2584 26 + +#zero padding to 3000 +def data2array(file): + #zero padding to file.shape[0] X 20 X 3000 + n=file.shape[0] + k=0 + array = np.repeat(0, n * 20 * 3000).reshape(n, 20, 3000) + filename=file[0] + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + for j in range(abs_mfcc.shape[1]): + array[k, :, j]=abs_mfcc[:, j] + k+=1 + return(array) + +trainData=data2array(trainfile) +testData=data2array(testfile) + +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 20, 3000) (2842, 20, 3000) (6631,) (2842,) + +""" +#csv다운로드 했는데 train, test 합쳐서 15GB라 안올리고 생략. +trainData2D=trainData.reshape(-1, 20*3000) +testData2D=testData.reshape(-1, 20*3000) +np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/trainData.csv', + trainData2D, delimiter=",") +np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/testData.csv', + testData2D, delimiter=",") +""" + + +#how many kinds of label? +print(len(np.unique(trainLabel))) #41 +print(len(np.unique(testLabel))) #41 + +#label string -> integer(0~40) +idx = np.unique(trainLabel) + +def Labeling(label): + r=pd.Series(label) + for i in range(len(idx)): + r[r.values==idx[i]]=i + return(r) + +trainLabel=Labeling(trainLabel) +testLabel=Labeling(testLabel) +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) + +#다시 돌릴때는 여기부터 +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.001 +training_epochs = 500 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 20, 3000], name="X") +X_sound = tf.reshape(X, [-1, 20, 3000, 1]) # 20*3000*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 20, 3000, 1) 3000=20*150 +W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 20, 20, 1],strides=[1, 20, 20, 1], padding='SAME') # l1 shape=(?, 14, 14, 32) +L1 = tf.nn.dropout(L1, p_keep_conv) +L1_flat= tf.reshape(L1, shape=[-1, 150*32]) + +# Final FC 4x4x128 inputs -> 10 outputs +W4 = tf.get_variable("W4", shape=[150*32, 625],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.elu(tf.matmul(L1_flat, W4)) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: testData[:100], Y: testLabel[:100].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + +A=np.array([1,2,3,4,9,1,2]) + + + +#테스트 할 때 샘플 추출 하는 방법 바꾸기 +""" +에폭 1000, lr 0.001, 정확도 32.6~32.9% +다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9% +""" \ No newline at end of file diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py new file mode 100644 index 0000000..31542b6 --- /dev/null +++ b/x_ksh/SA_cnn_ps3.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +import librosa +import soundfile as sf +import numpy as np + +#import labels +import tensorflow as tf +tf.set_random_seed(777) +import pandas as pd +#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') +train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') + +#train/test, Data/Label split +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(train, test_size = 0.3) +trainfile = train_set.values[:,0] +testfile = test_set.values[:,0] +trainLabel = train_set.values[:,1] +testLabel = test_set.values[:,1] + +#data load and extract mfcc (scaling indluded) +#path = '/Users/kimseunghyuck/desktop/audio_train/' +path = '/home/paperspace/Downloads/audio_train/' + +def see_how_long(file): + c=[] + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + #1025 X t 형태 + c.append(abs_mfcc.shape[1]) + return(c) + +#n=see_how_long(trainfile) +#print(np.max(n), np.min(n)) #2584 28 +#n2=see_how_long(testfile) +#print(np.max(n2), np.min(n2)) #2584 26 + +#zero padding to 3000 +def data2array(file): + #zero padding to file.shape[0] X 20 X 3000 + n=file.shape[0] + k=0 + array = np.repeat(0, n * 20 * 3000).reshape(n, 20, 3000) + filename=file[0] + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + for j in range(abs_mfcc.shape[1]): + array[k, :, j]=abs_mfcc[:, j] + k+=1 + return(array) + +trainData=data2array(trainfile) +testData=data2array(testfile) + +print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) +# (6631, 20, 3000) (2842, 20, 3000) (6631,) (2842,) + +""" +#csv다운로드 했는데 train, test 합쳐서 15GB라 안올리고 생략. +trainData2D=trainData.reshape(-1, 20*3000) +testData2D=testData.reshape(-1, 20*3000) +np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/trainData.csv', + trainData2D, delimiter=",") +np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/testData.csv', + testData2D, delimiter=",") +""" + + +#how many kinds of label? +print(len(np.unique(trainLabel))) #41 +print(len(np.unique(testLabel))) #41 + +#label string -> integer(0~40) +idx = np.unique(trainLabel) + +def Labeling(label): + r=pd.Series(label) + for i in range(len(idx)): + r[r.values==idx[i]]=i + return(r) + +trainLabel=Labeling(trainLabel) +testLabel=Labeling(testLabel) +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) + +#다시 돌릴때는 여기부터 +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.001 +training_epochs = 500 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 20, 3000], name="X") +X_sound = tf.reshape(X, [-1, 20, 3000, 1]) # 20*3000*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 20, 3000, 1) 3000=20*150 +W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 20, 20, 1],strides=[1, 20, 20, 1], padding='SAME') # l1 shape=(?, 14, 14, 32) +L1 = tf.nn.dropout(L1, p_keep_conv) +L1_flat= tf.reshape(L1, shape=[-1, 150*32]) + +# Final FC 4x4x128 inputs -> 10 outputs +W4 = tf.get_variable("W4", shape=[150*32, 625],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.elu(tf.matmul(L1_flat, W4)) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: testData[:100], Y: testLabel[:100].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + +#테스트 할 때 샘플 추출 하는 방법 바꾸기 +#list(array1, array2, ...) 이런식으로 구성해야겠음. 안그러면 너무 커짐. +#A=[np.array([1,2,3]), np.array([2,3,4,5,1])] +#아래처럼 구성해볼 생각. +""" +W1(20, 20) +for i in range(length): + L[i]=tanh(matmul(X, W1)) +max(L) + +에폭 1000, lr 0.001, 정확도 32.6~32.9% +다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9% +""" \ No newline at end of file diff --git a/x_ksh/sound_analysis5_cnn_ps.py b/x_ksh/sound_analysis5_cnn_ps.py index 1d936b3..a378de6 100644 --- a/x_ksh/sound_analysis5_cnn_ps.py +++ b/x_ksh/sound_analysis5_cnn_ps.py @@ -66,6 +66,17 @@ def data2array(file): print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) # (6631, 20, 3000) (2842, 20, 3000) (6631,) (2842,) +""" +#csv다운로드 했는데 train, test 합쳐서 15GB라 안올리고 생략. +trainData2D=trainData.reshape(-1, 20*3000) +testData2D=testData.reshape(-1, 20*3000) +np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/trainData.csv', + trainData2D, delimiter=",") +np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/testData.csv', + testData2D, delimiter=",") +""" + + #how many kinds of label? print(len(np.unique(trainLabel))) #41 print(len(np.unique(testLabel))) #41 From b99a6f0f25e7dd69df4844c52111bd749d79da71 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Fri, 27 Apr 2018 20:58:10 +0900 Subject: [PATCH 29/42] all --- x_ksh/SA_cnn_ps3.py | 1 + 1 file changed, 1 insertion(+) diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index 31542b6..1acb3cc 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -166,6 +166,7 @@ def Labeling(label): for i in range(length): L[i]=tanh(matmul(X, W1)) max(L) +윈도우를 끝까지 이동시켜서 그 중에 맥스값을 찾아내서 반환한 다음에 FC 를 거쳐서 softmax 에폭 1000, lr 0.001, 정확도 32.6~32.9% 다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9% From 1d6cac18a936fa67bf05f09ce34d892c6d7b8a41 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Fri, 27 Apr 2018 21:07:40 +0900 Subject: [PATCH 30/42] all --- x_ksh/README.md | 43 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/x_ksh/README.md b/x_ksh/README.md index 403203a..7011a9b 100644 --- a/x_ksh/README.md +++ b/x_ksh/README.md @@ -1,7 +1,48 @@ # Smallerhand; 김승혁 (KYLius-method) 진행상황 -sound_analysis2.py - 이거 하나만 보면 됨. +
    +아래로 갈수록 과거에 돌린 모델 (맨 위에 있는 게 현재 돌리고 있는 모델)
    +
    + +## 현재 (구상중이거나 돌리고 있는) 모델 +
    +SA_cnn_ps3.py  (코드 아직 짜고 있음. 아직 못 짬)
    +
    +list안에 array를 담아서 가변적인 길이의 mfcc output을 담은 다음에 CNN모델(아마 파이썬으로 코드 짜야 할 듯)에 넣고, +
    +20*20(n_mfcc를 20으로 한다고 가정할 때) 의 window 로 지나가면서 output을 배출. +
    +그 중에 max값을 뽑아낸다. 아주 간단한 구조지만 텐서플로우에 제공되는 함수가 없으므로 파이썬으로 짜야 할 것임. +
    +짜는 게 좀 어려울 수 있고, 원만히 돌아간다면 보다 정교화할 계획. +
    +장점1: 효율적임.
    +장점2: 필요한 정보만 얻을 수 있다.
    +장점3: 연속적인 흐름(파형)을 파악할 수 있다.
    +장점4: 시간이 앞의 모델보다 훨씬 적게 걸릴 것이다.
    +단점: 구현하기 어렵다.
    +
    + +## 지난 모델 2 (돌아가긴 함) +
    +sound_analysis5_cnn_ps.py
    +
    +0으로 전부 패딩(전처리과정) 해서 길이를 같게 만든 다음 CNN -> Fully Connectied -> Softmax +
    +정확도: 에폭 50에서 20%대. (낮음)
    +시간: 오래걸림
    +가능성: 아주 간단하게 conv2d하나 + FC 하나만 썼는데 20% 대이므로 정교하게 구성하면 높아질 수 있을 것 같음.
    +단점1: 데이터 크기가 너무 커짐. (csv로 다운받으니 전체 15GB 정도 됨)
    +단점2: 그래서 시간이 오래걸리고 메모리 관리가 힘듬
    +단점3: 같은 얘기지만 비효율적임.
    +
    + +## 지난 모델 1 (돌아가긴 함) +
    +sound_analysis2.py
    +sound_analysis3.py
    +
    import tensorflow as tf From 5c3f8ee777bc6a68fac98ac2177e5b9f25538418 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Sat, 28 Apr 2018 16:05:06 +0900 Subject: [PATCH 31/42] all --- PAPERS/README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/PAPERS/README.md b/PAPERS/README.md index 0eb421a..22d9ddf 100644 --- a/PAPERS/README.md +++ b/PAPERS/README.md @@ -1,6 +1,11 @@ # 참고자료 위에 pdf 파일 안 올린 건 링크 적음 +### - librosa: Audio and Music Signal Analysis in Python ★★★★ +http://conference.scipy.org/proceedings/scipy2015/pdfs/brian_mcfee.pdf +
    +python library librosa 0.4.0 에 대한 설명
    +
    ### - Spectrogram, Cepstrum and Mel-Frequency Analysis ★★★★★ http://www.speech.cs.cmu.edu/15-492/slides/03_mfcc.pdf From d68ccf04480b0ac87d4b706b5d914db817365c3e Mon Sep 17 00:00:00 2001 From: smallerhand Date: Sat, 28 Apr 2018 18:03:12 +0900 Subject: [PATCH 32/42] all --- x_ksh/SA_cnn.py | 203 ++++++++++++++++++++++++++++ x_ksh/SA_cnn_ps3.py | 133 ++++-------------- x_ksh/optx/checkpoint | 4 +- x_ksh/optx/optx.data-00000-of-00001 | Bin 542708 -> 12315680 bytes x_ksh/optx/optx.index | Bin 694 -> 549 bytes x_ksh/optx/optx.meta | Bin 72614 -> 66826 bytes 6 files changed, 233 insertions(+), 107 deletions(-) create mode 100644 x_ksh/SA_cnn.py diff --git a/x_ksh/SA_cnn.py b/x_ksh/SA_cnn.py new file mode 100644 index 0000000..6ed28c0 --- /dev/null +++ b/x_ksh/SA_cnn.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +import librosa +import soundfile as sf +import numpy as np + +#import labels +import tensorflow as tf +tf.set_random_seed(777) +import pandas as pd +#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') +train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') + +#train/test, Data/Label split +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(train, test_size = 0.3) +trainfile = train_set.values[:,0] +testfile = test_set.values[:,0] +trainLabel = train_set.values[:,1] +testLabel = test_set.values[:,1] + +#data load and extract mfcc (scaling indluded) +#path = '/Users/kimseunghyuck/desktop/audio_train/' +path = '/home/paperspace/Downloads/audio_train/' + +def see_how_long(file): + c=[] + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + #1025 X t 형태 + c.append(abs_mfcc.shape[1]) + return(c) + +#n=see_how_long(trainfile) +#print(np.max(n), np.min(n)) #2584 28 +#n2=see_how_long(testfile) +#print(np.max(n2), np.min(n2)) #2584 26 + +#zero padding to 3000 +def data2array(file): + #zero padding to file.shape[0] X 20 X 3000 + n=file.shape[0] + k=0 + array = np.repeat(0, n * 20 * 3000).reshape(n, 20, 3000) + filename=file[0] + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + for j in range(abs_mfcc.shape[1]): + array[k, :, j]=abs_mfcc[:, j] + k+=1 + return(array) + +def short_time_extract(file): + #zero padding to file.shape[0] X 20 X 3000 + n=file.shape[0] + array = np.repeat(0, n * 20 * 100).reshape(n, 20, 100) + k=0 + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + length=mfcc.shape[1] + abs_mfcc=np.abs(mfcc) + if length == 100: + array[k, :, :]=abs_mfcc + elif length < 100: + array[k, :, :length]=abs_mfcc + elif length > 100: + argmax=np.argmax(abs_mfcc, axis=1) + sample=[] + for i in range(np.max(argmax)): + sample.append(np.sum((argmax>=i) & (argmax integer(0~40) +idx = np.unique(trainLabel) + +def Labeling(label): + r=pd.Series(label) + for i in range(len(idx)): + r[r.values==idx[i]]=i + return(r) + +trainLabel=Labeling(trainLabel) +testLabel=Labeling(testLabel) +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) +#0 40 0 40 + +#다시 돌릴때는 여기부터 +tf.reset_default_graph() #그래프 초기화 + +# hyper parameters +learning_rate = 0.001 +training_epochs = 100 +batch_size = 100 +steps_for_validate = 5 + +#placeholder +X = tf.placeholder(tf.float32, [None, 20, 100], name="X") +X_sound = tf.reshape(X, [-1, 20, 100, 1]) # 20*100*1 (frequency, time, amplitude) +Y = tf.placeholder(tf.int32, [None, 1], name="Y") +Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) +p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") +p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") + +# L1 SoundIn shape=(?, 20, 100, 1) +W1 = tf.get_variable("W1", shape=[2, 10, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') +L1 = tf.nn.elu(L1) +L1 = tf.nn.max_pool(L1, ksize=[1, 4, 10, 1],strides=[1, 4, 10, 1], padding='SAME') # l1 shape=(?, 20, 100, 32) +L1 = tf.nn.dropout(L1, p_keep_conv) +L1_flat= tf.reshape(L1, shape=[-1, 5*10*32]) + +# Final FC 4x4x128 inputs -> 10 outputs +W4 = tf.get_variable("W4", shape=[5*10*32, 625],initializer=tf.contrib.layers.xavier_initializer()) +L4 = tf.nn.elu(tf.matmul(L1_flat, W4)) +L4 = tf.nn.dropout(L4, p_keep_hidden) +W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +b = tf.Variable(tf.random_normal([41])) +logits = tf.matmul(L4, W_o) + b + +# define cost/loss & optimizer +cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) +optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 +predict_op = tf.argmax(logits, 1, name="pred") + +# initialize +sess = tf.Session() +sess.run(tf.global_variables_initializer()) +saver = tf.train.Saver() + +# train my model +print('Learning started. It takes sometime.') +for epoch in range(training_epochs): + avg_cost = 0 + total_batch = int(len(trainData) / batch_size) + for i in range(total_batch): + batch_xs = trainData[i*batch_size:(i+1)*batch_size] + batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) + feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} + c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) + avg_cost += c / total_batch + print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) + if epoch % steps_for_validate == steps_for_validate-1: + correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) + accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + print('Accuracy:', sess.run(accuracy, feed_dict={ + X: testData[:100], Y: testLabel[:100].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') +print('Finished!') + +#테스트 할 때 샘플 추출 하는 방법 바꾸기 +#list(array1, array2, ...) 이런식으로 구성해야겠음. 안그러면 너무 커짐. +#A=[np.array([1,2,3]), np.array([2,3,4,5,1])] +#아래처럼 구성해볼 생각. +""" +W1(20, 20) +for i in range(length): + L[i]=tanh(matmul(X, W1)) +max(L) +윈도우를 끝까지 이동시켜서 그 중에 맥스값을 찾아내서 반환한 다음에 FC 를 거쳐서 softmax + +에폭 1000, lr 0.001, 정확도 32.6~32.9% +다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9% +""" \ No newline at end of file diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index 1acb3cc..69b21f9 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -6,93 +6,21 @@ @author: kimseunghyuck """ -import librosa -import soundfile as sf +#import important modules import numpy as np - -#import labels import tensorflow as tf tf.set_random_seed(777) import pandas as pd -#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') -train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') - -#train/test, Data/Label split -from sklearn.model_selection import train_test_split -train_set, test_set = train_test_split(train, test_size = 0.3) -trainfile = train_set.values[:,0] -testfile = test_set.values[:,0] -trainLabel = train_set.values[:,1] -testLabel = test_set.values[:,1] - -#data load and extract mfcc (scaling indluded) -#path = '/Users/kimseunghyuck/desktop/audio_train/' -path = '/home/paperspace/Downloads/audio_train/' - -def see_how_long(file): - c=[] - for filename in file: - y, sr = sf.read(path+filename, dtype='float32') - mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) - abs_mfcc=np.abs(mfcc) - #1025 X t 형태 - c.append(abs_mfcc.shape[1]) - return(c) - -#n=see_how_long(trainfile) -#print(np.max(n), np.min(n)) #2584 28 -#n2=see_how_long(testfile) -#print(np.max(n2), np.min(n2)) #2584 26 -#zero padding to 3000 -def data2array(file): - #zero padding to file.shape[0] X 20 X 3000 - n=file.shape[0] - k=0 - array = np.repeat(0, n * 20 * 3000).reshape(n, 20, 3000) - filename=file[0] - for filename in file: - y, sr = sf.read(path+filename, dtype='float32') - mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) - abs_mfcc=np.abs(mfcc) - for j in range(abs_mfcc.shape[1]): - array[k, :, j]=abs_mfcc[:, j] - k+=1 - return(array) - -trainData=data2array(trainfile) -testData=data2array(testfile) +trainData = pd.read_csv('/home/paperspace/Downloads/trainData.csv') +trainData = trainData.reshape(-1, 20, 100) +testData = pd.read_csv('/home/paperspace/Downloads/testData.csv') +testData = trainData.reshape(-1, 20, 100) +trainLabel = pd.read_csv('/home/paperspace/Downloads/trainLabel.csv') +testLabel = pd.read_csv('/home/paperspace/Downloads/testLabel.csv') print(trainData.shape, testData.shape, trainLabel.shape, testLabel.shape) -# (6631, 20, 3000) (2842, 20, 3000) (6631,) (2842,) - -""" -#csv다운로드 했는데 train, test 합쳐서 15GB라 안올리고 생략. -trainData2D=trainData.reshape(-1, 20*3000) -testData2D=testData.reshape(-1, 20*3000) -np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/trainData.csv', - trainData2D, delimiter=",") -np.savetxt('/Users/kimseunghyuck/desktop/git/daegon/KYLius-method/x_ksh/testData.csv', - testData2D, delimiter=",") -""" - - -#how many kinds of label? -print(len(np.unique(trainLabel))) #41 -print(len(np.unique(testLabel))) #41 - -#label string -> integer(0~40) -idx = np.unique(trainLabel) - -def Labeling(label): - r=pd.Series(label) - for i in range(len(idx)): - r[r.values==idx[i]]=i - return(r) - -trainLabel=Labeling(trainLabel) -testLabel=Labeling(testLabel) -print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) +# (6631, 20, 100) (2842, 20, 100) (6631,) (2842,) #다시 돌릴때는 여기부터 tf.reset_default_graph() #그래프 초기화 @@ -104,26 +32,34 @@ def Labeling(label): steps_for_validate = 5 #placeholder -X = tf.placeholder(tf.float32, [None, 20, 3000], name="X") -X_sound = tf.reshape(X, [-1, 20, 3000, 1]) # 20*3000*1 (frequency, time, amplitude) +X = tf.placeholder(tf.float32, [None, 20, 100], name="X") +X_sound = tf.reshape(X, [-1, 20, 100, 1]) # 20*100*1 (frequency, time, amplitude) Y = tf.placeholder(tf.int32, [None, 1], name="Y") Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") -# L1 SoundIn shape=(?, 20, 3000, 1) 3000=20*150 -W1 = tf.get_variable("W1", shape=[2, 20, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +# L1 SoundIn shape=(?, 20, 100, 1) +W1 = tf.get_variable("W1", shape=[2, 10, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 20, 20, 1],strides=[1, 20, 20, 1], padding='SAME') # l1 shape=(?, 14, 14, 32) +L1 = tf.nn.max_pool(L1, ksize=[1, 4, 10, 1],strides=[1, 4, 10, 1], padding='SAME') # l1 shape=(?, 20, 100, 32) L1 = tf.nn.dropout(L1, p_keep_conv) -L1_flat= tf.reshape(L1, shape=[-1, 150*32]) - -# Final FC 4x4x128 inputs -> 10 outputs -W4 = tf.get_variable("W4", shape=[150*32, 625],initializer=tf.contrib.layers.xavier_initializer()) +L1_flat= tf.reshape(L1, shape=[-1, 5*10*32]) + +# L2 Input shape=(?,5,10,32) +W2 = tf.get_variable("W2", shape=[2, 10, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') +L2 = tf.nn.elu(L2) +L2 = tf.nn.max_pool(L2, ksize=[1, 5, 5, 1],strides=[1, 5, 5, 1], padding='SAME') # l1 shape=(?, 5, 10, 64) +L2 = tf.nn.dropout(L2, p_keep_conv) +L2_flat= tf.reshape(L1, shape=[-1, 2*64]) + +# Final FC 2*64 inputs -> 10 outputs +W4 = tf.get_variable("W4", shape=[2*64, 256],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L1_flat, W4)) L4 = tf.nn.dropout(L4, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) +W_o = tf.get_variable("W_o", shape=[256,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L4, W_o) + b @@ -152,22 +88,9 @@ def Labeling(label): if epoch % steps_for_validate == steps_for_validate-1: correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) + x=np.random.choice(testLabel.shape[0], 300, replace=False) print('Accuracy:', sess.run(accuracy, feed_dict={ - X: testData[:100], Y: testLabel[:100].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) + X: testData[x], Y: testLabel[x].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') print('Finished!') -#테스트 할 때 샘플 추출 하는 방법 바꾸기 -#list(array1, array2, ...) 이런식으로 구성해야겠음. 안그러면 너무 커짐. -#A=[np.array([1,2,3]), np.array([2,3,4,5,1])] -#아래처럼 구성해볼 생각. -""" -W1(20, 20) -for i in range(length): - L[i]=tanh(matmul(X, W1)) -max(L) -윈도우를 끝까지 이동시켜서 그 중에 맥스값을 찾아내서 반환한 다음에 FC 를 거쳐서 softmax - -에폭 1000, lr 0.001, 정확도 32.6~32.9% -다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9% -""" \ No newline at 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    -## 현재 (구상중이거나 돌리고 있는) 모델 +## 현재 돌리고 있는 거 (잘 돌아감)
    -SA_cnn_ps3.py  (코드 아직 짜고 있음. 아직 못 짬)
    +preparing_data.py - 데이터 전처리
    +SA_cnn_ps3.py - 모델
    +
    + +preparing_data.py +
    +이 코드로 trainData.csv, trainLabel.csv, testData.csv, testLabel.csv 를 만들어 저장함. (전체 약 600MB)
    +sklearn 모듈 이용, 7:3으로 train/test split.
    +see_how_long 은 mfcc 뽑았을 때 파일별로 길이가 어느 정도인지 확인한 것. (이후 안쓰이니 신경 안써도 됨)
    +
    +n_mfcc=20으로 mfcc로 변환한 후, (길이가 26~2584로 들쭉날쭉한데) 100으로 통일함.
    +어떻게 했냐면,
    +(short_time_extrat 함수)
    +- 길이가 (드물겠지만) 100이면 그대로 array에 넣음.
    +- 길이가 100보다 작으면 나머지는 0으로 채운 뒤 array에 넣음.
    +- 길이가 100보다 크면 (이 부분이 약간 복잡한데),
    +20개 특성 주파수 각각 중 절대값의 최대값을 가지고 있는 구간(np.argmax 이용)을 뽑아서,
    +i~i+100 구간을 정할 때 최대값이 가장 많이 포함되는 i 값 (0< i < 데이터 길이) 을 찾은 뒤,
    +i~i+100 까지만 잘라서 넣음.
    +
    +            argmax=np.argmax(abs_mfcc, axis=1)
    +            sample=[]
    +            for i in range(np.max(argmax)):
    +                 sample.append(np.sum((argmax>=i) & (argmax 
    +
    +데이터 라벨링은 0~40의 숫자로 치환함.
    +(결과물 낼 때는 이부분에서 영어: 숫자 저장한 csv도 있어야 할 것임. 여기서는 생략)
    +
    +이렇게 해서 (추후 모델 돌릴 때 편의를 위해) 저장함.
    +
    + +SA_cnn_ps3.py +
    +preparing_data.py 로 저장시킨 데이터 일단 불러온 뒤 reshape.
    +shape이 맞는지 확인.
    +conv2d L1 : 20*100*1 로 입력 받아서 10*20*32 로 방출
    +    (윈도우 사이즈 =  2 * 10, 맥스풀 사이즈 = 2 *5)
    +conv2d L2 : 10*20*32 로 받아서 4*7*64 로 방출
    +    (win = 2 * 2, max_pool = 3 * 3)
    +conv2d L3 : 4* 7* 64 로 받아서 2*3*128 로 (flat하게) 방출
    +    (win = 2 * 2, max_pool = 3 * 3)
    +FC L4: 2*3*64 로 받아서 41 로 방출
    +    (2*3*128 -> 615 -> 41)
    +
    +정확도: 51~60%
    +
    + +## 일단 보류 중인 모델 (코드 짜는 데 오래 걸릴 것 같아서 시간상 보류) +
    +코드 아직 못 짬
     
    list안에 array를 담아서 가변적인 길이의 mfcc output을 담은 다음에 CNN모델(아마 파이썬으로 코드 짜야 할 듯)에 넣고,
    diff --git a/x_ksh/SA_cnn.py b/x_ksh/SA_cnn.py deleted file mode 100644 index 6ed28c0..0000000 --- a/x_ksh/SA_cnn.py +++ /dev/null @@ -1,203 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Apr 26 18:53:10 2018 - -@author: kimseunghyuck -""" - -import librosa -import soundfile as sf -import numpy as np - -#import labels -import tensorflow as tf -tf.set_random_seed(777) -import pandas as pd -#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') -train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') - -#train/test, Data/Label split -from sklearn.model_selection import train_test_split -train_set, test_set = train_test_split(train, test_size = 0.3) -trainfile = train_set.values[:,0] -testfile = test_set.values[:,0] -trainLabel = train_set.values[:,1] -testLabel = test_set.values[:,1] - -#data load and extract mfcc (scaling indluded) -#path = '/Users/kimseunghyuck/desktop/audio_train/' -path = '/home/paperspace/Downloads/audio_train/' - -def see_how_long(file): - c=[] - for filename in file: - y, sr = sf.read(path+filename, dtype='float32') - mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) - abs_mfcc=np.abs(mfcc) - #1025 X t 형태 - c.append(abs_mfcc.shape[1]) - return(c) - -#n=see_how_long(trainfile) -#print(np.max(n), np.min(n)) #2584 28 -#n2=see_how_long(testfile) -#print(np.max(n2), np.min(n2)) #2584 26 - -#zero padding to 3000 -def data2array(file): - #zero padding to file.shape[0] X 20 X 3000 - n=file.shape[0] - k=0 - array = np.repeat(0, n * 20 * 3000).reshape(n, 20, 3000) - filename=file[0] - for filename in file: - y, sr = sf.read(path+filename, dtype='float32') - mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) - abs_mfcc=np.abs(mfcc) - for j in range(abs_mfcc.shape[1]): - array[k, :, j]=abs_mfcc[:, j] - k+=1 - return(array) - -def short_time_extract(file): - #zero padding to file.shape[0] X 20 X 3000 - n=file.shape[0] - array = np.repeat(0, n * 20 * 100).reshape(n, 20, 100) - k=0 - for filename in file: - y, sr = sf.read(path+filename, dtype='float32') - mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) - length=mfcc.shape[1] - abs_mfcc=np.abs(mfcc) - if length == 100: - array[k, :, :]=abs_mfcc - elif length < 100: - array[k, :, :length]=abs_mfcc - elif length > 100: - argmax=np.argmax(abs_mfcc, axis=1) - sample=[] - for i in range(np.max(argmax)): - sample.append(np.sum((argmax>=i) & (argmax integer(0~40) -idx = np.unique(trainLabel) - -def Labeling(label): - r=pd.Series(label) - for i in range(len(idx)): - r[r.values==idx[i]]=i - return(r) - -trainLabel=Labeling(trainLabel) -testLabel=Labeling(testLabel) -print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) -#0 40 0 40 - -#다시 돌릴때는 여기부터 -tf.reset_default_graph() #그래프 초기화 - -# hyper parameters -learning_rate = 0.001 -training_epochs = 100 -batch_size = 100 -steps_for_validate = 5 - -#placeholder -X = tf.placeholder(tf.float32, [None, 20, 100], name="X") -X_sound = tf.reshape(X, [-1, 20, 100, 1]) # 20*100*1 (frequency, time, amplitude) -Y = tf.placeholder(tf.int32, [None, 1], name="Y") -Y_onehot=tf.reshape(tf.one_hot(Y, 41), [-1, 41]) -p_keep_conv = tf.placeholder(tf.float32, name="p_keep_conv") -p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") - -# L1 SoundIn shape=(?, 20, 100, 1) -W1 = tf.get_variable("W1", shape=[2, 10, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) -L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') -L1 = tf.nn.elu(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 4, 10, 1],strides=[1, 4, 10, 1], padding='SAME') # l1 shape=(?, 20, 100, 32) -L1 = tf.nn.dropout(L1, p_keep_conv) -L1_flat= tf.reshape(L1, shape=[-1, 5*10*32]) - -# Final FC 4x4x128 inputs -> 10 outputs -W4 = tf.get_variable("W4", shape=[5*10*32, 625],initializer=tf.contrib.layers.xavier_initializer()) -L4 = tf.nn.elu(tf.matmul(L1_flat, W4)) -L4 = tf.nn.dropout(L4, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[625,41],initializer=tf.contrib.layers.xavier_initializer()) -b = tf.Variable(tf.random_normal([41])) -logits = tf.matmul(L4, W_o) + b - -# define cost/loss & optimizer -cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels= Y_onehot)) -optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # 아담버젼 -predict_op = tf.argmax(logits, 1, name="pred") - -# initialize -sess = tf.Session() -sess.run(tf.global_variables_initializer()) -saver = tf.train.Saver() - -# train my model -print('Learning started. It takes sometime.') -for epoch in range(training_epochs): - avg_cost = 0 - total_batch = int(len(trainData) / batch_size) - for i in range(total_batch): - batch_xs = trainData[i*batch_size:(i+1)*batch_size] - batch_ys = trainLabel[i*batch_size:(i+1)*batch_size].reshape(-1, 1) - feed_dict = {X: batch_xs, Y: batch_ys, p_keep_conv: .7, p_keep_hidden: .5} - c, _ = sess.run([cost, optimizer], feed_dict=feed_dict) - avg_cost += c / total_batch - print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost)) - if epoch % steps_for_validate == steps_for_validate-1: - correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y_onehot, 1)) - accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - print('Accuracy:', sess.run(accuracy, feed_dict={ - X: testData[:100], Y: testLabel[:100].reshape(-1, 1), p_keep_conv: 1, p_keep_hidden: 1})) - save_path = saver.save(sess, '/home/paperspace/Downloads/optx/optx') -print('Finished!') - -#테스트 할 때 샘플 추출 하는 방법 바꾸기 -#list(array1, array2, ...) 이런식으로 구성해야겠음. 안그러면 너무 커짐. -#A=[np.array([1,2,3]), np.array([2,3,4,5,1])] -#아래처럼 구성해볼 생각. -""" -W1(20, 20) -for i in range(length): - L[i]=tanh(matmul(X, W1)) -max(L) -윈도우를 끝까지 이동시켜서 그 중에 맥스값을 찾아내서 반환한 다음에 FC 를 거쳐서 softmax - -에폭 1000, lr 0.001, 정확도 32.6~32.9% -다른 조건 같고 keep_prop=0.8, 정확도 36.9~37.9% -""" \ No newline at end of file diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index de65398..b22482d 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -60,7 +60,7 @@ L3 = tf.nn.dropout(L3, p_keep_conv) L3_flat= tf.reshape(L3, shape=[-1, 2*3*128]) -# Final FC 2*64 inputs -> 10 outputs +# Final FC 2*3*128 inputs -> 41 outputs W4 = tf.get_variable("W4", shape=[2*3*128, 615],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.nn.dropout(L4, p_keep_hidden) diff --git a/x_ksh/preparing_data.py b/x_ksh/preparing_data.py new file mode 100644 index 0000000..b82e0ba --- /dev/null +++ b/x_ksh/preparing_data.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Apr 26 18:53:10 2018 + +@author: kimseunghyuck +""" + +import librosa +import soundfile as sf +import numpy as np + +#import labels +import tensorflow as tf +tf.set_random_seed(777) +import pandas as pd +#train = pd.read_csv('/Users/kimseunghyuck/desktop/sound_train.csv') +train = pd.read_csv('/home/paperspace/Downloads/audio_train.csv') + +#train/test, Data/Label split +from sklearn.model_selection import train_test_split +train_set, test_set = train_test_split(train, test_size = 0.3) +trainfile = train_set.values[:,0] +testfile = test_set.values[:,0] +trainLabel = train_set.values[:,1] +testLabel = test_set.values[:,1] + +#data load and extract mfcc (scaling indluded) +#path = '/Users/kimseunghyuck/desktop/audio_train/' +path = '/home/paperspace/Downloads/audio_train/' + +def see_how_long(file): + c=[] + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc=librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + abs_mfcc=np.abs(mfcc) + #1025 X t 형태 + c.append(abs_mfcc.shape[1]) + return(c) + +#n=see_how_long(trainfile) +#print(np.max(n), np.min(n)) #2584 28 +#n2=see_how_long(testfile) +#print(np.max(n2), np.min(n2)) #2584 26 + +def short_time_extract(file): + #zero padding to file.shape[0] X 20 X 3000 + n=file.shape[0] + array = np.repeat(0, n * 20 * 100).reshape(n, 20, 100) + k=0 + for filename in file: + y, sr = sf.read(path+filename, dtype='float32') + mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20) + length=mfcc.shape[1] + abs_mfcc=np.abs(mfcc) + if length == 100: + array[k, :, :]=abs_mfcc + elif length < 100: + array[k, :, :length]=abs_mfcc + elif length > 100: + argmax=np.argmax(abs_mfcc, axis=1) + sample=[] + for i in range(np.max(argmax)): + sample.append(np.sum((argmax>=i) & (argmax integer(0~40) +idx = np.unique(trainLabel) + +def Labeling(label): + r=pd.Series(label) + for i in range(len(idx)): + r[r.values==idx[i]]=i + return(r) + +trainLabel=Labeling(trainLabel) +testLabel=Labeling(testLabel) +print(min(trainLabel), max(trainLabel), min(testLabel), max(testLabel)) +#0 40 0 40 + +""" +#csv downdload totally about 600MB +trainData2D=trainData.reshape(-1, 20*100) +testData2D=testData.reshape(-1, 20*100) +np.savetxt('/Users/kimseunghyuck/desktop/trainData.csv', + trainData2D, delimiter=",") +np.savetxt('/Users/kimseunghyuck/desktop/testData.csv', + testData2D, delimiter=",") +np.savetxt('/Users/kimseunghyuck/desktop/trainLabel.csv', + trainLabel, delimiter=",") +np.savetxt('/Users/kimseunghyuck/desktop/testLabel.csv', + testLabel, delimiter=",") +""" From 53a0c329ee5d92a9fa3650713b2ee523db6134b2 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Sat, 28 Apr 2018 20:33:52 +0900 Subject: [PATCH 40/42] all --- x_ksh/SA_cnn_ps3.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index b22482d..32d09e9 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -39,29 +39,29 @@ p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") # L1 SoundIn shape=(?, 20, 100, 1) -W1 = tf.get_variable("W1", shape=[2, 10, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +W1 = tf.get_variable("W1", shape=[2, 2, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 2, 5, 1],strides=[1, 2, 5, 1], padding='SAME') +L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) -# L2 Input shape=(?,10,20,32) +# L2 Input shape=(?,10,50,32) W2 = tf.get_variable("W2", shape=[2, 2, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) -L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') L2 = tf.nn.dropout(L2, p_keep_conv) -# L3 Input shape=(?,4,7,64) +# L3 Input shape=(?,5,25,64) W3 = tf.get_variable("W3", shape=[2, 2, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) -L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') +L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') L3 = tf.nn.dropout(L3, p_keep_conv) -L3_flat= tf.reshape(L3, shape=[-1, 2*3*128]) +L3_flat= tf.reshape(L3, shape=[-1, 3*13*128]) # Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[2*3*128, 615],initializer=tf.contrib.layers.xavier_initializer()) +W4 = tf.get_variable("W4", shape=[3*13*128, 615],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.nn.dropout(L4, p_keep_hidden) W_o = tf.get_variable("W_o", shape=[615,41],initializer=tf.contrib.layers.xavier_initializer()) @@ -112,4 +112,8 @@ lr=0.0002, epoch = 300 p_keep_conv, p_keep_hidden = 0.8, 0.7 accuracy: 51~60% +4) window를 정사각형 모양으로 바꿈 +lr=0.0002, epoch = 300 +p_keep_conv, p_keep_hidden = 0.8, 0.7 +accuracy: """ \ No newline at end of file From f6bdbbe2fe89e23392d8f51da193bd536e93be0c Mon Sep 17 00:00:00 2001 From: smallerhand Date: Sat, 28 Apr 2018 21:10:51 +0900 Subject: [PATCH 41/42] all --- x_ksh/SA_cnn_ps3.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index 32d09e9..3c308b8 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -115,5 +115,9 @@ 4) window를 정사각형 모양으로 바꿈 lr=0.0002, epoch = 300 p_keep_conv, p_keep_hidden = 0.8, 0.7 -accuracy: +win : (2, 10), (2,4), (2,3) +max_pool : (2,5), (3,3), (3,3) +accuracy: 53~65% + + """ \ No newline at end of file From 5b77b741a2d57a1bba793360cb2ba3085db71d18 Mon Sep 17 00:00:00 2001 From: smallerhand Date: Sat, 28 Apr 2018 21:25:33 +0900 Subject: [PATCH 42/42] all --- x_ksh/SA_cnn_ps3.py | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/x_ksh/SA_cnn_ps3.py b/x_ksh/SA_cnn_ps3.py index 3c308b8..de5dc55 100644 --- a/x_ksh/SA_cnn_ps3.py +++ b/x_ksh/SA_cnn_ps3.py @@ -39,32 +39,32 @@ p_keep_hidden = tf.placeholder(tf.float32, name="p_keep_hidden") # L1 SoundIn shape=(?, 20, 100, 1) -W1 = tf.get_variable("W1", shape=[2, 2, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) +W1 = tf.get_variable("W1", shape=[2, 10, 1, 32],initializer=tf.contrib.layers.xavier_initializer()) L1 = tf.nn.conv2d(X_sound, W1, strides=[1, 1, 1, 1], padding='SAME') L1 = tf.nn.elu(L1) -L1 = tf.nn.max_pool(L1, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L1 = tf.nn.max_pool(L1, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L1 = tf.nn.dropout(L1, p_keep_conv) -# L2 Input shape=(?,10,50,32) -W2 = tf.get_variable("W2", shape=[2, 2, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) +# L2 Input shape=(?,7,34,32) +W2 = tf.get_variable("W2", shape=[2, 10, 32, 64],initializer=tf.contrib.layers.xavier_initializer()) L2 = tf.nn.conv2d(L1, W2, strides=[1, 1, 1, 1], padding='SAME') L2 = tf.nn.elu(L2) -L2 = tf.nn.max_pool(L2, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L2 = tf.nn.max_pool(L2, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L2 = tf.nn.dropout(L2, p_keep_conv) -# L3 Input shape=(?,5,25,64) -W3 = tf.get_variable("W3", shape=[2, 2, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) +# L3 Input shape=(?,3,12,64) +W3 = tf.get_variable("W3", shape=[2, 10, 64, 128],initializer=tf.contrib.layers.xavier_initializer()) L3 = tf.nn.conv2d(L2, W3, strides=[1, 1, 1, 1], padding='SAME') L3 = tf.nn.elu(L3) -L3 = tf.nn.max_pool(L3, ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1], padding='SAME') +L3 = tf.nn.max_pool(L3, ksize=[1, 3, 3, 1],strides=[1, 3, 3, 1], padding='SAME') L3 = tf.nn.dropout(L3, p_keep_conv) -L3_flat= tf.reshape(L3, shape=[-1, 3*13*128]) +L3_flat= tf.reshape(L3, shape=[-1, 4*128]) # Final FC 2*3*128 inputs -> 41 outputs -W4 = tf.get_variable("W4", shape=[3*13*128, 615],initializer=tf.contrib.layers.xavier_initializer()) +W4 = tf.get_variable("W4", shape=[4*128, 512],initializer=tf.contrib.layers.xavier_initializer()) L4 = tf.nn.elu(tf.matmul(L3_flat, W4)) L4 = tf.nn.dropout(L4, p_keep_hidden) -W_o = tf.get_variable("W_o", shape=[615,41],initializer=tf.contrib.layers.xavier_initializer()) +W_o = tf.get_variable("W_o", shape=[512,41],initializer=tf.contrib.layers.xavier_initializer()) b = tf.Variable(tf.random_normal([41])) logits = tf.matmul(L4, W_o) + b @@ -111,13 +111,15 @@ 3) con2d layer * 3 + FC lr=0.0002, epoch = 300 p_keep_conv, p_keep_hidden = 0.8, 0.7 +win : (2, 10), (2,2), (2,2) +max_pool : (2,5), (3,3), (3,3) accuracy: 51~60% -4) window를 정사각형 모양으로 바꿈 -lr=0.0002, epoch = 300 -p_keep_conv, p_keep_hidden = 0.8, 0.7 +4) 3에서 윈도우 조절 win : (2, 10), (2,4), (2,3) max_pool : (2,5), (3,3), (3,3) accuracy: 53~65% - - +5) 3에서 윈도우 조절2 +win : (2, 10), (2,10), (2,10) +max_pool : (3,3), (3,3), (3,3) + """ \ No newline at end of file