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篇名
Voice Activity Detection Based on Auto-Correlation Function Using Wavelet Transform and Teager Energy Operator
作者 Wu, Bing-fei (Wu, Bing-fei)Wang, Kun-ching (Wang, Kun-ching)
中文摘要
In this paper, a new robust wavelet-based voice activity detection (VAD) algorithm derived from the discrete wavelet transform (DWT) and Teager energy operation (TEO) processing is presented. We decompose the speech signal into four subbands by using the DWT. By means of the multi-resolution analysis property of the DWT, the voiced, unvoiced, and transient components of speech can be distinctly discriminated. In order to develop a robust feature parameter called the speech activity envelope (SAE), the TEO is then applied to the DWT coefficients of each subband. The periodicity of speech signal is further exploited by using the subband signal auto-correlation function (SSACF) for. Experimental results show that the proposed SAE feature parameter can extract the speech activity under poor SNR conditions and that it is also insensitive to variable-level of noise.
起訖頁 87-100
關鍵詞 Voice activity detectionAuto-correlationWaveletTeager energy
刊名 中文計算語言學期刊  
期數 200603 (11:1期)
出版單位 中華民國計算語言學學會
該期刊-上一篇 A Fast Framework for the Constrained Mean Trajectory Segment Model by Avoidance of Redundant Computation on Segment
 

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