From Zzz's to Data: Building an AI-Powered Snore Recognition System with YAMNet 😴🚀

#machinelearning #android #tensorflow #healthtech

We’ve all been there: waking up feeling like you’ve been hit by a truck, even after eight hours of "sleep." Often, the culprit is hidden in the silence (or lack thereof) of the night. Sleep apnea and chronic snoring aren't just annoying for your partner; they are serious health indicators.

In this tutorial, we are going to dive deep into audio classification and digital health engineering. We'll leverage YAMNet, a deep net that predicts 521 audio classes, to build a system that can distinguish between a peaceful night, a heavy snorer, and a concerning cough. By the end of this post, you'll understand how to implement an end-to-end pipeline using TensorFlow Hub, Librosa, and Android/Kotlin.

The Architecture: How Audio AI Works 🏗️

Before we write a single line of code, let’s visualize how we transform raw sound waves into actionable health insights. Our system follows a classic Digital Signal Processing (DSP) to Inference pipeline.

graph TD
    A[Raw Audio Input .wav] --> B[Resampling & Normalization]
    B --> C[Feature Extraction: Mel Spectrograms]
    C --> D[YAMNet Pre-trained Model]
    D --> E{Transfer Learning Layer}
    E --> F[Class: Snore]
    E --> G[Class: Cough]
    E --> H[Class: Ambient Noise]
    F --> I[Android Dashboard / Risk Assessment]

Prerequisites 🛠️

To follow along, you'll need:

TensorFlow Hub: To access the pre-trained YAMNet weights.
Librosa: The Swiss Army knife for audio preprocessing in Python.
Android Studio: If you want to deploy this as a mobile health tracker using Kotlin.

Step 1: Preprocessing with Librosa 🎼

YAMNet expects audio sampled at exactly 16,000 Hz. Most phone microphones record at 44.1kHz or 48kHz, so resampling is our first hurdle.

import librosa
import numpy as np

def preprocess_audio(file_path):
    # Load audio file and resample to 16kHz
    audio, sr = librosa.load(file_path, sr=16000)

    # Normalize the audio to a range of [-1.0, 1.0]
    audio = librosa.util.normalize(audio)

    # Ensure it's mono channel (YAMNet requirement)
    if len(audio.shape) > 1:
        audio = np.mean(audio, axis=1)

    return audio

# Example usage
waveform = preprocess_audio("night_recording_001.wav")
print(f"Waveform shape: {waveform.shape}")

Step 2: Fine-Tuning YAMNet with TensorFlow Hub 🧠

YAMNet is great, but it’s trained on the YouTube-8M dataset. To make it a specialized medical tool, we use Transfer Learning. We freeze the early layers and train a new "head" to specifically recognize "Snoring" vs "Coughing."

import tensorflow as tf
import tensorflow_hub as hub

# Load the YAMNet model from TF Hub
model = hub.load('https://tfhub.dev/google/yamnet/1')

# Define a custom classifier head
def build_health_classifier(yamnet_model):
    inputs = tf.keras.layers.Input(shape=(16000,), dtype=tf.float32)
    # Get the embedding from YAMNet
    scores, embeddings, spectrogram = yamnet_model(inputs)

    # Add our custom layers
    x = tf.keras.layers.Dense(256, activation='relu')(embeddings)
    x = tf.keras.layers.Dropout(0.3)(x)
    outputs = tf.keras.layers.Dense(3, activation='softmax')(x) # Snore, Cough, Noise

    return tf.keras.Model(inputs, outputs)

health_model = build_health_classifier(model)
health_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Step 3: Deploying to Android (Kotlin) 📱

Once we export our model to TFLite, we can run inference on-device. This is crucial for privacy—no one wants their bedroom recordings sent to a cloud server!

// Android/Kotlin snippet for TFLite Inference
class SnoreDetector(context: Context) {
    private var tflite: Interpreter? = null

    init {
        val model = FileUtil.loadMappedFile(context, "snore_model.tflite")
        tflite = Interpreter(model)
    }

    fun classifyAudio(audioData: FloatArray): String {
        val output = Array(1) { FloatArray(3) }
        tflite?.run(audioData, output)

        // Find index with max probability
        val labels = listOf("Snore", "Cough", "Ambient")
        val maxIndex = output[0].indices.maxByOrNull { output[0][it] } ?: -1
        return labels[maxIndex]
    }
}

Scaling for Production: The "Official" Way 🥑

Building a prototype is easy, but making it robust enough for a clinical setting requires advanced signal processing and data validation patterns.

If you are looking for advanced architectural patterns or want to see how this integrates into a full-scale healthcare backend, I highly recommend checking out the technical deep-dives at WellAlly Blog. They cover production-ready AI deployments and mobile health (mHealth) security standards that are essential if you plan to move beyond a local script.

Conclusion 🌙

Identifying sleep risks doesn't require a full sleep lab anymore. With YAMNet and TensorFlow, we can turn a standard smartphone into a powerful diagnostic tool. By focusing on local processing (Edge AI), we ensure user privacy while providing meaningful health data.

What's next for your project?

[ ] Add a "Sleep Cycle" graph based on audio intensity.
[ ] Integrate with Apple HealthKit or Google Fit.
[ ] Implement a low-pass filter to remove fan noise.

Have you tried building audio classifiers before? Let’s chat in the comments! 👇

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