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software-integration-demos/android-keyword-spotting.md

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+---
+description: >-
+  Build a voice-activated assistant on your smartphone with Edge Impulse, TensorFlow Lite, and Android Studio for efficient wake-word detection.
+---
+
+# Building a Voice-Activated Assistant on Your Smartphone: A Step-by-Step Guide 
+
+Created By: Haziqa Sajid
+
+## Introduction
+
+The AI revolution, particularly in the domain of large language models (LLMs) at present, is incredibly impressive. However, these models are energy-hungry, which makes it challenging to run them nonstop on smartphones without quickly draining the battery. That’s why smaller, energy-efficient models are critical for real-world applications, especially for systems that need to stay on all the time.
+
+As Pete Warden and Daniel Situnayake highlight in [TinyML](https://tinymlbook.com/):
+
+> "Google was running neural networks that were just 14 kilobytes (KB) in size! They needed to be so small because they were running on the digital signal processors (DSPs) present in most Android phones, continuously listening for the 'OK Google' wake words..."
+
+This kind of breakthrough shows what’s possible when you focus on keeping models small and efficient.
+
+In this guide, we’ll follow the same philosophy: you’ll build your own custom wake-word detector that runs directly on your phone, using tools like **Edge Impulse**, **TensorFlow Lite**, and **Android Studio**. The system will be optimized to listen for a trigger phrase like “Neo” with minimal power usage, no cloud calls, no bulky models, just fast, local inference.
+
+## Edge AI and Android Integration
+
+Running the model directly on the phone means audio never leaves the device, so privacy stays protected. Latency is low, too. There's no cloud round‑trip, which means faster reaction times. It’s ideal for a wake‑word experience . And yes, even if you're offline, you can still trigger the wake word, especially handy in low‑connectivity scenarios like fieldwork.
+
+However, the question of bringing custom voice recognition directly onto your Android phone would scare many people. That’s where Edge Impulse and on-device AI shine. In this section, we will answer the following questions:
+
+- **Why is Edge Impulse a Natural Fit for Mobile Voice Recognition?**
+
+Edge Impulse provides an end-to-end platform tailored for edge AI, especially audio. Its workflow lets you record samples right from your phone, build a keyword‑spotting model using the built-in MFCC (we’ll check that out later) processing block, and train it to recognize your own wake word. Trained impulse then becomes a self-contained C++ signal‑processing pipeline that’s ready for on-device use.
+
+- **How Edge Impulse Works with Android via TensorFlow Lite?**
+
+Once your model is trained and tested, Edge Impulse lets you export it in TensorFlow Lite format. If you're building in native C++, you can include the Edge Impulse‑generated C++ library using the NDK and CMake. Or, if you prefer a Java/Kotlin route, just load the `.tflite` model and run inference through TFLite’s Interpreter API.
+
+In this guide, we will focus on building in native C++ and include it in our Android application.
+
+## Practical Implementation
+
+Now that we’ve covered the _why_, let’s dive into the _how_. In this section, you’ll learn how to build your own custom wake word detection system using Edge Impulse and deploy it on an Android device. We’ll walk through every stage, which includes setting up your tools, gathering voice data, training your model, testing, and then, most importantly, deploying it on Android.
+
+### Setting Up Your Development Environment
+
+First, you need a few tools before you can start building. Head over to [Edge Impulse](https://studio.edgeimpulse.com) and create a free account. Once you're in, start a new project:
+
+![Fig 1: Creating a New Project in Edge Impulse](../.gitbook/assets/android-keyword-spotting/image1a.png)
+
+We can also choose “Keyword Spotting” as the project type. Doing so will give you the right building blocks for voice recognition.
+
+Next, install [Android Studio](https://developer.android.com/studio) if you haven’t already done so. Since we’re going to use Edge Impulse’s C++ SDK for running inference on-device, you’ll also need to set up the Android NDK. 
+
+If you install the latest version of Android Studio, it will automatically install the required NDK. Otherwise, you can do that directly from the SDK Manager. Just look for the “NDK” and “CMake” options under the SDK Tools tab and install them.
+
+![Fig 2: The SDK Tools Window Showing the NDK Option](../.gitbook/assets/android-keyword-spotting/image1a.png)
+
+To make your life easier, Edge Impulse provides an [Android inferencing example on GitHub](https://github.com/edgeimpulse/example-standalone-inferencing-android). Clone that repo, and it has all the scaffolding you need. You'll be modifying this code to run your trained model.
+
+Don’t forget to prepare your phone as well. Enable Developer Mode, turn on USB debugging, and connect it to your machine. This setup lets you install and debug the app directly on your phone using Android Studio. It also gives you access to log output via **Logcat**. This is incredibly helpful when you're testing voice recognition behavior in real-time. 
+
+You can also use an Android Emulator from Android Studio. Just ensure that it is configured with microphone access enabled.
+
+### Collecting Your Voice Data
+
+The very first step in any machine learning project is collecting data, and Edge Impulse makes this part surprisingly smooth. In the left-hand menu, click on **Data Acquisition** to get started.
+
+![Fig 3: Data Acquisition Page in Edge Impulse](../.gitbook/assets/android-keyword-spotting/image3a.png)
+
+Edge Impulse gives you several options to bring in data. You can upload files directly, pull samples from public projects, or even generate synthetic data for modalities like images or audio. For this project, we’ll be recording data live.
+
+To do that, click **"Connect a device"** and then choose **"Use your computer."** This allows you to record audio samples using your laptop’s microphone right from your browser:
+
+![Fig 4: Collecting Data From Your Computer](../.gitbook/assets/android-keyword-spotting/image4a.png)
+
+After this, you can record your sound with different labels:
+
+![Fig 5: Collecting Data by Recording Audio](../.gitbook/assets/android-keyword-spotting/image5a.png)
+
+To train our model, we recorded 1-second clips of our chosen wake word: “Neo.” We also wanted to ignore the wrong words. 
+
+To achieve this, we recorded 1-second clips of random background noise, including footsteps, door creaks, and the hum of a fan. These “noise” samples teach the model what not to react to. We repeated this over and over until we had about two minutes of clean voice samples. That’s not a ton of data, but it's enough to get a functional prototype going.
+
+Of course, more data equals better performance, so aim for 5–10 minutes if you have time. All this data gets uploaded to Edge Impulse and labeled accordingly. From there, you’re ready to build your dataset.
+
+### Building Your Dataset
+
+Once your samples are uploaded and labeled, Edge Impulse helps you organize them. It automatically splits your dataset into training (80%) and testing (20%). It’s a good default for most use cases.
+
+
+
+