Let’s be honest: no one wants their private nighttime "soundtrack" (a.k.a. snoring or heavy breathing) being uploaded to a corporate server for "analysis." Yet, monitoring sleep health is crucial, especially for detecting potential Sleep Apnea or respiratory distress.
In this tutorial, we are building Sleep Guardian, a high-performance Edge AI system. We’ll combine the feature extraction power of Whisper-tiny with the sequential modeling of Temporal Convolutional Networks (TCN) to create a real-time, localized monitoring system. By leveraging Raspberry Pi and Docker, we ensure this runs 24/7 without ever needing an internet connection.
This is the ultimate project for anyone interested in Real-time Audio Classification, Edge Computing, and Privacy-focused AI.
The Architecture 🏗️
The system operates in a pipeline: capturing raw audio, extracting high-level latent features using a pre-trained transformer encoder, and then classifying those patterns over time.
