Robust voice activity detection using gammatone filtering and entropy

Voice activity detector (VAD) is used to detect the presence or absence of human voice in a signal. A robust VAD algorithm is essential to distinguish human voice in a noisy acoustic signal. There were many recent works in development of robust VAD which focus on unsupervised features extraction such as temporal variation, signal-to-noise ratio in [1] and etc. However, these methods are typically sensitive to nonstationary noise especially under low SNR. To overcome these problems, this paper presents a robust voice activity detection (VAD) method via a combination of gammatone filtering and entropy as an information-theoretic measure in the detection algorithm. The performance of the proposed algorithm is tested using speech signals from TIMIT test corpus with additive noise at varying degrees of signal-to-noise ratio. The results show that the proposed robust VAD outperforms other existing methods in terms of detection accuracy.