Robust Voice Activity Detection Based on Discrete Wavelet Transform

Robust Voice Activity Detection Based on Discrete Wavelet Transform Kun-Ching Wang Department of Information Technology & Communication Shin Chien University kunching@mail.kh.usc.edu.tw Abstract This paper mainly addresses the problem of determining voice activity in presence of noise, especially in a dynamically varying background noise. The proposed voice activity detection algorithm is based on structure of three-layer wavelet decomposition. Appling auto-correlation function into each subband exploits the fact that intensity of periodicity is more significant in sub-band domain than that in full-band domain. In addition, Teager energy operator (TEO) is used to eliminate the noise components from the wavelet coefficients on each subband. Experimental results show that the proposed wavelet-based algorithm is prior to others and can work in a dynamically varying background noise. Keywords: voice activity detection, auto-correlation function, wavelet transform, Teager energy operator 1. Introduction Voice activity detection (VAD) refers to the ability of distinguishing speech from noise and is an integral part of a variety of speech communication systems, such as speech coding, speech recognition, hand-free telephony, and echo cancellation. Although the existed VAD algorithms performed reliably, their feature parameters are almost depended on the energy level and sensitive to noisy environments [1-4]. So far, a wavelet-based VAD is rather less discussed although wavelet analysis is much suitable for speech property. S.H. Chen et al. [5] shown that the proposed VAD is based on wavelet transform and has an excellent performance. In fact, their approach is not suitable for practical application such as variable-level of noise conditions. Besides, a great computing time is needed for accomplishing wavelet reconstruction to decide whether is speech-active or not. Compared with Chen's VAD approach, the proposed decision of VAD only depends on three-layer wavelet decomposition. This approach does not need any computing time to waste the wavelet reconstruction. In addition, the four non-uniform subbands are generated from the wavelet-based approach and the well-known "auto-correlaction function (ACF)" is adopted to detect the periodicity of subband. We refer the ACF defined in subband domain as subband auto-correlation function (SACF). Due to that periodic property is mainly focused on low frequency bands, so we let the low frequency bands have high resolution to enhance the periodic property by decomposing only low band on each layer. In addition to the SACF, enclosed herein the Teager energy operator (TEO) is regarded as a pre-processor for SACF. The TEO is a powerful nonlinear operator and has been successfully used in various speech processing applications [6-7]. F. Jabloun et al. [8] displayed that TEO can suppress the car engine noise and be easily implemented through time domain in Mel-scale subband. The later experimental result will prove that the TEO can further enhance the detection of subband periodicity. To accurately count the intensity of periodicity from the envelope of the SACF, the Mean-Delta (MD) method [9] is utilized on each subband. The MD-based feature parameter has been presented for the robust development of VAD, but is not performed well in the non-stationary noise shown in the followings. Eventually, summing up the …

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