Kun-Ching Wang
Voice activity detection, spectral entropy, noise estimate
To obtain reliable performance of Voice Activity Detector (VAD) algorithm, the straight lines on spectrogram of speech-activity being robust against noise is characterized by an entropy-based measure in this paper. A measure of entropy will be defined on the energy domain of harmonic subband. It is shown that the entropy-based measure is well suited for detecting speech in white or quasi-white noises, but will perform poorly for coloured noises. To compensate the limitation, the refined minima controlled recursive averaging, which be updated quickly and accurately even given rapidly increas- ing levels of noise, is required to desensitize the measure of entropy to various types of noise. Consequently, the proposed VAD algorithm is shown significantly outperform the commonly used energy-based algorithm when SNR drops rapidly, and moreover is insensitive to the changing-level of noise. Experimental results demonstrate that the performance of the proposed VAD is comparable to modern standard VADs such that ITU-T G.729B and ETSI front-end VAD or statistical model-based VADs.
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