Voice Activity Detection with Array Signal Processing in the Wavelet Domain

Yusuke HIOKA; Nozomu HAMADA

Voice Activity Detection with Array Signal Processing in the Wavelet Domain

Yusuke HIOKA, Nozomu HAMADA

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In speech enhancement with adaptive microphone array, the voice activity detection (VAD) is indispensable for the adaptation control. Even though many VAD methods have been proposed as a pre-processor for speech recognition and compression, they can hardly discriminate nonstationary interferences which frequently exist in real environment. In this research, we propose a novel VAD method with array signal processing in the wavelet domain. In that domain we can integrate the temporal, spectral and spatial information to achieve robust voice activity discriminability for a nonstationary interference arriving from close direction of speech. The signals acquired by microphone array are at first decomposed into appropriate subbands using wavelet packet to extract its temporal and spectral features. Then directionality check and direction estimation on each subbands are executed to do VAD with respect to the spatial information. Computer simulation results for sound data demonstrate that the proposed method keeps its discriminability even for the interference arriving from close direction of speech.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.11 pp.2802-2811

Publication Date: 2003/11/01

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Type of Manuscript: PAPER

Category: Engineering Acoustics

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Yusuke HIOKA, Nozomu HAMADA, "Voice Activity Detection with Array Signal Processing in the Wavelet Domain" in IEICE TRANSACTIONS on Fundamentals, vol. E86-A, no. 11, pp. 2802-2811, November 2003, doi: .
Abstract: In speech enhancement with adaptive microphone array, the voice activity detection (VAD) is indispensable for the adaptation control. Even though many VAD methods have been proposed as a pre-processor for speech recognition and compression, they can hardly discriminate nonstationary interferences which frequently exist in real environment. In this research, we propose a novel VAD method with array signal processing in the wavelet domain. In that domain we can integrate the temporal, spectral and spatial information to achieve robust voice activity discriminability for a nonstationary interference arriving from close direction of speech. The signals acquired by microphone array are at first decomposed into appropriate subbands using wavelet packet to extract its temporal and spectral features. Then directionality check and direction estimation on each subbands are executed to do VAD with respect to the spatial information. Computer simulation results for sound data demonstrate that the proposed method keeps its discriminability even for the interference arriving from close direction of speech.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e86-a_11_2802/_p

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@ARTICLE{e86-a_11_2802,
author={Yusuke HIOKA, Nozomu HAMADA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Voice Activity Detection with Array Signal Processing in the Wavelet Domain},
year={2003},
volume={E86-A},
number={11},
pages={2802-2811},
abstract={In speech enhancement with adaptive microphone array, the voice activity detection (VAD) is indispensable for the adaptation control. Even though many VAD methods have been proposed as a pre-processor for speech recognition and compression, they can hardly discriminate nonstationary interferences which frequently exist in real environment. In this research, we propose a novel VAD method with array signal processing in the wavelet domain. In that domain we can integrate the temporal, spectral and spatial information to achieve robust voice activity discriminability for a nonstationary interference arriving from close direction of speech. The signals acquired by microphone array are at first decomposed into appropriate subbands using wavelet packet to extract its temporal and spectral features. Then directionality check and direction estimation on each subbands are executed to do VAD with respect to the spatial information. Computer simulation results for sound data demonstrate that the proposed method keeps its discriminability even for the interference arriving from close direction of speech.},
keywords={},
doi={},
ISSN={},
month={November},}

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TY - JOUR
TI - Voice Activity Detection with Array Signal Processing in the Wavelet Domain
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2802
EP - 2811
AU - Yusuke HIOKA
AU - Nozomu HAMADA
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2003
AB - In speech enhancement with adaptive microphone array, the voice activity detection (VAD) is indispensable for the adaptation control. Even though many VAD methods have been proposed as a pre-processor for speech recognition and compression, they can hardly discriminate nonstationary interferences which frequently exist in real environment. In this research, we propose a novel VAD method with array signal processing in the wavelet domain. In that domain we can integrate the temporal, spectral and spatial information to achieve robust voice activity discriminability for a nonstationary interference arriving from close direction of speech. The signals acquired by microphone array are at first decomposed into appropriate subbands using wavelet packet to extract its temporal and spectral features. Then directionality check and direction estimation on each subbands are executed to do VAD with respect to the spatial information. Computer simulation results for sound data demonstrate that the proposed method keeps its discriminability even for the interference arriving from close direction of speech.
ER -