A Novel Approach Based on Adaptive Long-Term Sub-Band Entropy and Multi-Thresholding Scheme for Detecting Speech Signal

Abstract

Conventional entropy measure is derived from full-band (range from 0Hz to 4kHz); however, it can not clearly describe the spectrum variability during voice-activity. Here we propose a novel concept of adaptive long-term sub-band entropy (ALT-SubEnpy) measure and combine it with a multi-thresholding scheme for voice activity detection. In detail, the ALT-SubEnpy measure developed with four part parameters of sub-entropy which uses different long-term spectral window length at each part. Consequently, the proposed ALT-SubEnpy-based algorithm recursively updates the four adaptive thresholds on each part. The proposed ALT-SubEnpy-based VAD method is shown to be an effective method while working at variable noise-level condition.