NOVA-VAD

NOVA-VAD is a lightweight, explainable Voice Activity Detector for noisy real-world audio.

It predicts SPEECH or NO SPEECH and returns a confidence score plus the top audio features that influenced the decision.

GitHub: https://github.com/monishmal3375/nova-vad

X: https://x.com/Nova_vad

Why It Exists

Voice activity detection is the first quality gate for ASR, diarization, call transcription, robotics, edge audio, and realtime voice agents.

Bad VAD can cause:

• clipped speech
• false speech segments
• wasted compute
• noisy transcripts
• worse realtime agent behavior

NOVA-VAD is built around a practical wedge: noisy-audio performance, lightweight deployment, and explainable decisions.

Current Benchmark

Tested on 100 held-out noisy-audio files from UrbanSound8K categories such as traffic, sirens, jackhammers, AC units, and construction noise.

These results describe this benchmark setup only.

Note: the full benchmark environment installs heavier baseline libraries so the repo can compare against them. The NOVA-VAD classifier itself is a feature-based scikit-learn ensemble.

How It Works

raw audio -> denoiser -> 150+ audio features -> ensemble classifier -> prediction + explanation

Feature families include:

• MFCCs and deltas
• zero crossing rate
• RMS energy patterns
• spectral flux
• spectral centroid and rolloff
• harmonic/percussive ratio
• tempo/rhythm
• mel spectrogram statistics
• silence ratio

The current model uses a Random Forest + Gradient Boosting ensemble.

Quick Start

git clone https://github.com/monishmal3375/nova-vad.git
cd nova-vad
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python3 download_data.py
python3 -m src.pipeline

Explain one prediction:

python3 -m src.explainer data/clean_speech/speech_001.wav

Try your own audio after local models are saved:

python3 -m src.explainer path/to/your_audio.wav

Run benchmark:

python3 -m src.benchmark

Example Output

NOVA-VAD EXPLANATION
File:        speech_001.wav
Prediction:  SPEECH
Confidence:  93.47%

Why this decision was made:
MFCC Delta 1 std      (10.63%) -> HIGH spectral change rate, dynamic audio like speech
MFCC Delta 2 std      ( 6.14%) -> HIGH acceleration, rapidly changing audio
Silence ratio          ( 5.92%) -> mix of speech and pauses
Spectral centroid std  ( 4.27%) -> shifting frequency center
Mel mean               ( 3.50%) -> normal speech level

Limitations

• The benchmark is scoped to the current noisy-audio test setup.
• More datasets and real production audio are needed.
• Streaming support exists but is still being improved.
• The project is early and packaging is not finished yet.
• Do not use private call recordings or sensitive speech data without permission.

Contribute

The most useful contributions are hard noisy-audio cases, benchmark results, packaging help, and streaming improvements.

Open an issue or PR on GitHub:

https://github.com/monishmal3375/nova-vad