Robust Voice Activity Detection Using Adaptive Sub-band Spectral Entropy

In this article, I present a robust voice activity detection method utilizing adaptive sub-band spectral entropy. This approach was independently developed by me and aims to provide valuable insights for researchers and practitioners. Adaptive sub-band spectral entropy represents an effective signal processing technique that accurately identifies speech endpoints within audio signals, enabling improved analysis and understanding of speech data. The algorithm typically involves partitioning the frequency spectrum into adaptive sub-bands, calculating entropy measures for each sub-band, and applying dynamic thresholding to distinguish speech segments from background noise. Key implementation aspects include FFT-based spectral analysis, entropy computation using probability density functions, and adaptive threshold adjustment mechanisms. This robust endpoint detection method achieves accurate speech boundary identification under various environmental conditions, providing a reliable foundation for speech processing and speech recognition applications. The code implementation would likely involve functions for signal framing, spectral decomposition, entropy calculation, and decision logic for endpoint classification. I hope this article offers inspiration and serves as a useful reference for your research and practical work in speech signal processing.