SleepSentry: Privacy-First Sleep Apnea Detection on Raspberry Pi using Whisper and Librosa

Sleep apnea is often called a "silent killer," affecting millions of people worldwide who remain undiagnosed. While many mobile apps claim to track sleep, they often rely on uploading sensitive bedroom audio to the cloud—a massive privacy nightmare. In this tutorial, we are building SleepSentry, an edge-computing solution that performs Sleep Apnea Detection and snoring classification locally on a Raspberry Pi. By leveraging Audio Signal Processing with Librosa and efficient inference with TensorFlow Lite, we ensure that your raw audio never leaves the device. We only process features, not recordings, keeping your data 100% private. The Architecture: From Sound Waves to Insights To achieve real-time classification on low-power hardware, we separate the pipeline into feature extraction and lightweight inference. We use Faster-Whisper for contextual audio analysis (like identifying sleep talking) and Librosa for the heavy lifting of Fast Fourier Transforms (FFT). graph TD A[USB Microphone