改进的基于小波变换的语音活动检测算法

Improved Voice Activity Detector Based on the Wavelet Transform

提出了一种改进的基于小波变换的语音活动检测算法。这种新算法能够在不同的时间和尺度上计算用于语音活动检测的参数 ,根据这些参数得到稳健的语音活动决策。实验表明 ,新算法与ITUG .72 9附录B相比 ,能够更准确地检测到语音活动 ,语音活动剪切率大为减少 ;同时新算法对于不同类型的背景噪声 ,即使全局信噪比在 10dB以下也具有较好的性能。

Owing to the flexibility of time_frequency resolution of wavelet transform,we present an improved voice activity detector (VAD) based on wavelet transform.Robust parameters in different scale and time resolution are computed for VAD decision,such as silence measure,stability measure of amplitude spectrum between adjacent frames,background noise measure of different frequency band,time_domain stability measure of scale 1. The silence measure is used to detect the existence of silence in the input frame.The stability measure of amplitude spectrum between adjacent frames is adopted to give a rough decision of the detection of stable noise based on the assumption that background noise is stable.If current input frame is noise,the energy of every frequency band is below the average of background noise energy threshold over long time.We divide the signal bandwidth into several scales by wavelet transform,and calculate the background noise measure of different scale.In low scale the input signal changes rapidly,and the variety of short time energy will be removed with long window.We calculate the mean square error of short time energy,and get time_domain stability measure from detail coefficients of scale 1.With these measures,we make the VAD decision. Compared with G.729 Annex B,the authors can detect the voice activity more accurately and reduce the ratio of speech clipping using the new algorithm.And the improved algorithm can achieve robust performance for different background noise,even in serious low signal_to_noise environment about 10dB.