Keyword spotting on Android

git clone https://github.com/edgeimpulse/example-android-inferencing.git
cd example-android-inferencing/example_kws

cd app/src/main/cpp/tflite
sh download_tflite_libs.sh  # or .bat for Windows

app/src/main/cpp/
├── edge-impulse-sdk/
├── model-parameters/
├── tflite-model/
├── native-lib.cpp
└── CMakeLists.txt (existing)

<uses-permission android:name="android.permission.RECORD_AUDIO" />
<uses-feature android:name="android.hardware.microphone" />

// MainActivity.kt
private fun startAudioRecording() {
    val bufferSize = AudioRecord.getMinBufferSize(
        SAMPLE_RATE,
        AudioFormat.CHANNEL_IN_MONO,
        AudioFormat.ENCODING_PCM_16BIT
    )
    
    audioRecord = AudioRecord(
        MediaRecorder.AudioSource.MIC,
        SAMPLE_RATE,
        AudioFormat.CHANNEL_IN_MONO,
        AudioFormat.ENCODING_PCM_16BIT,
        bufferSize
    )
    
    audioRecord.startRecording()
    
    // Read audio in background thread
    Thread {
        val buffer = ShortArray(bufferSize)
        while (isRecording) {
            val read = audioRecord.read(buffer, 0, buffer.size)
            if (read > 0) {
                processAudioBuffer(buffer, read)
            }
        }
    }.start()
}

private val audioRingBuffer = RingBuffer(16000) // 1 second at 16kHz

private fun processAudioBuffer(buffer: ShortArray, size: Int) {
    // Add to ring buffer
    audioRingBuffer.write(buffer, size)
    
    // Run inference when buffer is full
    if (audioRingBuffer.isFull()) {
        val features = audioRingBuffer.read()
        val result = runInference(features)
        
        runOnUiThread {
            updateUI(result)
        }
    }
}

// native-lib.cpp
extern "C" JNIEXPORT jobject JNICALL
Java_com_example_kws_EIClassifierAudio_run(
    JNIEnv* env, jobject, jshortArray audioData) {
    
    // Convert audio to float features
    jshort* audio = env->GetShortArrayElements(audioData, nullptr);
    int length = env->GetArrayLength(audioData);
    
    std::vector<float> features(length);
    for (int i = 0; i < length; i++) {
        features[i] = (float)audio[i] / 32768.0f;
    }
    
    // Create signal
    signal_t signal;
    signal.total_length = features.size();
    signal.get_data = &get_feature_data;
    
    // Run classifier
    ei_impulse_result_t result = {0};
    run_classifier(&signal, &result, false);
    
    // Return result object
    return createResultObject(env, result);
}

private fun updateUI(result: ClassificationResult) {
    // Find highest confidence prediction
    val topResult = result.classification.maxByOrNull { it.score } ?: return
    
    if (topResult.score > CONFIDENCE_THRESHOLD) {
        // Show detected keyword
        keywordTextView.text = topResult.label
        confidenceTextView.text = "${(topResult.score * 100).toInt()}%"
        
        // Highlight detection
        detectionIndicator.setBackgroundColor(Color.GREEN)
        
        // Trigger action (optional)
        onKeywordDetected(topResult.label)
    } else {
        keywordTextView.text = "Listening..."
        detectionIndicator.setBackgroundColor(Color.GRAY)
    }
}