Skip to content

Latest commit

 

History

History
38 lines (35 loc) · 2.53 KB

README.md

File metadata and controls

38 lines (35 loc) · 2.53 KB
Raw

Embedded sound classifier

Overview

This application uses the on-board MEMS microphone to collect audio samples, analyze them using a pre-trained neural network and send their classification on the serial port. A desktop python script takes care of reading the results.

Requirements

  • STM32F4 Discovery board (project has been developed using STM32F407VGT6)
  • For sound clasification (normal usage):
    • Python 3. You can check your version with python --V
    • pyserial: pip install pyserial
    • A RS232-USB cable
  • For neural network training:
    • Python 3, pyserial and RS232-USB cable as in previous case
    • Keras 2.2.4: pip install keras==2.2.4
    • Tensorflow 2.0.0-alpha0: pip install tensorflow==2.0.0-alpha0
    • GCC
  • For pre-trained Keras model to C conversion:
    • STMCubeMX
    • X-CUBE-AI: can be installed from within STMCubeMX
  • For embedded software compilation:

How to use

  • For sound classification (normal usage):
    1. Connect the serial cable pins to PA2 (board TX) and PA3 (board RX)
    2. Connect the board through USB cable
    3. Launch the client with python client.py serial_port_name, replacing serial_port_name with the name of the serial port (i.e /dev/tty, COM1)
    4. Press the board user button, do the desired sounds and press again the button to stop recording
  • For neural network training:
    1. Compile the FFT extraction program with gcc FFT_extract.c -o FFT_extract
    2. Connect the cables as in previous case
    3. Launch the FFT reciever with python FFT_receive.py serial_port_name
    4. Press the user button, do the desired sounds and press again the button to stop recording
    5. The results will be in the file fft.csv. They must be manually classified according to what they are: one last column has to be added and it must contain value 0 for silence, 1 for whistle or 2 for clap
    6. Go into the neural-network folder, place the new data in training_data.csv and run python trainer.py. The pre-trained model will output to file model.h5
  • For pre-trained Keras model to C library conversion: everything is explained in the docs/x-cube-ai.pdf file, provided by ST.
  • For embedded software compilation: use command make in the miosix-kernel folder or compile using your preferred CMake compatible IDE