Voice Activity Detection (VAD) is one of those foundational problems in audio processing that sounds simple — figure out when someone is talking and when they're not — but turns out to be surprisingly tricky in practice. Background noise, varying speaker volumes, and different recording environments all conspire to make this harder than it seems.

This repository implements two distinct approaches to VAD from first principles:

• Linear classifier: A straightforward approach that uses feature extraction (likely energy and zero-crossing rate) combined with a linear decision boundary to separate speech from silence.
• Statistical model classifier: A more sophisticated method that models the statistical distributions of speech and non-speech segments, potentially using Gaussian models or similar probabilistic frameworks to make detection decisions.

What makes this repo interesting is the side-by-side comparison of two fundamentally different paradigms for the same problem. The linear classifier represents the "keep it simple" school of thought — fast, interpretable, and easy to deploy. The statistical model takes a more principled probabilistic approach that can adapt better to varying noise conditions but comes with more complexity.

This kind of comparative implementation is genuinely useful for learning. Rather than just reading about the theoretical differences between discriminative and generative approaches, you can run both on the same audio data and see where each one succeeds or fails. It's the kind of hands-on experiment that solidifies understanding far better than textbook diagrams.

Who would benefit from this repo:

• Students taking signal processing or machine learning courses who want concrete, runnable examples of VAD techniques
• Audio engineers prototyping preprocessing pipelines who need a lightweight VAD module without pulling in heavy dependencies
• Hobbyists building voice-controlled projects on resource-constrained hardware where a simple linear classifier might be preferable to a neural network

VAD is also a critical preprocessing step in larger systems — automatic transcription, speaker diarization, and voice assistants all depend on accurately detecting when speech begins and ends. Understanding these fundamentals pays dividends across many downstream applications.