Spectral Subtraction-Based Voice Activity Detection

Spectral subtraction-based voice activity detection employs multiple signal processing techniques including speech enhancement, noise reduction, spectral subtraction, windowing, and endpoint detection. To improve the accuracy and robustness of voice activity detection, we implement a series of signal processing operations. Initially, we perform speech enhancement through spectral analysis and amplitude adjustment to improve signal quality and clarity. Subsequently, noise reduction is applied using techniques like spectral gating or Wiener filtering to purify the speech signal for better analysis. The core spectral subtraction algorithm is implemented by estimating noise spectrum during non-speech segments and subtracting it from the input signal's power spectrum. For optimal time-frequency representation, we apply windowing functions (such as Hamming or Hanning windows) with overlap-add processing to minimize spectral leakage. Finally, voice activity detection algorithms using energy-based thresholds or statistical models identify speech start and end points by analyzing features like short-term energy, zero-crossing rate, or spectral centroid. By integrating these methods with proper parameter tuning and frame-based processing, we achieve effective voice activity detection with accurate results.