Robust Voice Activity Detection Using Adaptive Sub-band Spectral Entropy

Robust voice activity detection using adaptive sub-band spectral entropy. This method employs a novel adaptive sub-band spectral entropy algorithm that enables reliable voice activity detection across various environmental conditions. The algorithm analyzes energy distribution patterns within different frequency sub-bands of speech signals, combined with adaptive entropy calculations, to effectively identify speech onset and offset points. Through preprocessing techniques and parameter optimization, we have significantly improved algorithm performance and accuracy. Key implementation aspects include: sub-band decomposition using filter banks or wavelet transforms, entropy threshold adaptation based on signal-to-noise ratio estimation, and dynamic parameter adjustment for environmental noise compensation. The proposed method demonstrates substantial potential for practical applications, offering significant benefits to speech processing and speech recognition systems by providing more accurate endpoint detection in challenging acoustic environments.