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Voice activity detection algorithm using perceptual wavelet entropy neighbor slope

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Lee, Gihyoun | Na, Sung Dae | Cho, Jin-Ho | Kim, Myoung Nam;

Affiliations: Department of Medical & Biological Engineering, Graduate School, Kyungpook National University, 680, Gukchaebosang-ro, Jung-gu, Daegu 700-842, Korea | School of Electronics Engineering, College of IT Engineering, Kyungpook National University, 680, Gukchaebosang-ro, Jung-gu, Daegu 700-842, Korea | Department of Biomedical Engineering, School of Medicine, Kyungpook National University, 680, Gukchaebosang-ro, Jung-gu, Daegu 700-842, Korea

Note: [] Corresponding author: Myoung Nam Kim, Department of Biomedical Engineering, School of Medicine, Kyungpook National University, 680, Gukchaebosang-ro, Jung-gu, Daegu 700-842, Korea. Tel.: +82-53-200-5266; Fax: +82-53-427-5539; E-mail: [email protected].

Keywords: Voice activity detection, wavelet transform, wavelet decomposition, neighbor slope, entropy

DOI: 10.3233/BME-141152

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3295-3301, 2014

Published: 2014
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Abstract

This paper presents a voice activity detection (VAD) approach using a perceptual wavelet entropy neighbor slope (PWENS) in a low signal-to-noise (SNR) environment and with a variety of noise types. The basis for our study is to use acoustic features that have large entropy variance for each wavelet critical band. The speech signal is decomposed by the proposed perceptual wavelet packet decomposition (PWPD), and the VAD function is extracted by PWENS. Finally, VAD is decided by the proposed VAD decision rule using two memory buffers. In order to evaluate the performance of the VAD decision, many speech samples and a variety of SNR conditions were used in the experiment. The performance of the VAD decision is confirmed using objective indexes such as a graph of the VAD decision and the relative error rate.

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