頁籤選單縮合
題 名 | Voice Activity Detection Based on Auto-Correlation Function Using Wavelet Transform and Teager Energy Operator |
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作 者 | Wu, Bing-fei; Wang, Kun-ching; | 書刊名 | International Journal of Computational Linguistics & Chinese Language Processing |
卷 期 | 11:1 民95.03 |
頁 次 | 頁87-100 |
分類號 | 312.13 |
關鍵詞 | Voice activity detection; Auto-correlation; Wavelet; Teager energy; |
語 文 | 英文(English) |
英文摘要 | 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. |
本系統中英文摘要資訊取自各篇刊載內容。