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Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 32-144
Full Name: Kun-Ching Wang
Position: Assistant Professor
Age: ON
Sex: Male
Address: No. 200, University Rd, Neimen Shiang, Kaohsiung, Taiwan, 845, R.O.C
Country: TAIWAN
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E-mail address: kunching@mail.kh.usc.edu.tw
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Title of the Paper: A Wavelet-Based Voice Activity Detection Algorithm in Variable-Level Noise Environment
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Number of paper pages: 11
Abstract: In this paper, a novel entropy-based voice activity detection (VAD) algorithm is presented in variable-level noise environment. Since the frequency energy of different types of noise focuses on different frequency subband, the effect of corrupted noise on each frequency subband is different. It is found that the seriously obscured frequency subbands have little word signal information left, and are harmful for detecting voice activity segment (VAS). First, we use bark-scale wavelet decomposition (BSWD) to split the input speech into 24 critical subbands. In order to discard the seriously corrupted frequency subband, a method of adaptive frequency subband extraction (AFSE) is then applied to only use the frequency subband. Next, we propose a measure of entropy defined on the spectrum domain of selected frequency subband to form a robust voice feature parameter. In addition, unvoiced is usually eliminated. An unvoiced detection is also integrated into the system to i!
mprove the intelligibility of voice. Experimental results show that the performance of this algorithm is superior to the G729B and other entropy-based VAD especially for variable-level background noise.
Keywords: Voice activity detection, Bark-scale wavelet decomposition, Adaptive frequency subband extraction
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