🗣️ Voice Activity Detection (VAD)
Voice Activity Detection (VAD) is the task of identifying whether a segment of audio contains human speech or not. It is a crucial pre-processing step in many real-time audio applications including speech recognition, telecommunication systems, and smart devices.
🧠 Problem Formulation
The input signal \(y(t)\) is a mixture of:
- Speech segments that we want to detect
- Background noise (e.g., traffic, music, keyboard typing, etc.)
- Silence or non-speech audio
The goal of VAD is to estimate a binary mask \(m(t)\) such that:
🔍 Why VAD Matters
VAD enables:
- Efficient transmission in voice communication systems (e.g., VoIP, cellular)
- Noise-aware processing for speech enhancement
- Accurate speech-to-text transcription by filtering out silence
- Trigger mechanisms in voice-controlled devices (e.g., smart assistants)
📊 Clean vs. Noisy Audio
In real-world conditions, VAD must operate under:
- Clean speech: well-recorded and noise-free audio
- Noisy speech: corrupted with background interference
- Reverberant environments: echo and reflections
Robust VAD models detect voice reliably even in challenging conditions.
🎯 VAD Target
Given a time-domain signal \(y(t)\), the VAD model computes a probability or binary decision over frames (e.g., every 10 ms) indicating speech presence.
The output can be visualized as a mask overlay on the spectrogram or a framewise binary vector.
🧰 soundKIT for VAD
soundKIT provides an end-to-end VAD pipeline that includes:
- TFRecord-based synthetic data creation with speech + noise + reverb
- Frame-level feature extraction (e.g., log-power spectrogram)
- Deep learning model training (e.g., CRNN)
- Real-time evaluation and visualization
- Export to embedded systems (TFLite, C header)
Robust and low-latency VAD is key to voice-first interfaces, on-device AI, and power-efficient audio systems.