Time-frequency analysis for voice activity detection

Authors:
Tuan Van Pham;Marián Képesi;Gernot Kubin;Luis Weruaga;Milan Sigmund;Tomas Dostál
Affiliations:
Signal Proc. & Speech Comm. Lab, Graz University of Technology, Graz, Austria;Signal Proc. & Speech Comm. Lab, Graz University of Technology, Graz, Austria;Signal Proc. & Speech Comm. Lab, Graz University of Technology, Graz, Austria;Commission for Scientific Visualization, Austrian Academy of Sciences, Vienna, Austria;Institute of Radio Electronics, Brno University of Technology, Brno, Czech Republic;Institute of Radio Electronics, Brno University of Technology, Brno, Czech Republic
Venue:
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Year:
2006

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Abstract

This paper introduces two different ways of time-frequency representations for voice activity detection (VAD). The first method is based on the chirp-based spectral representation of the signal, while the second method is based on wavelet decomposition. Not only this is the first implementation of the Fan-Chirp Transform for VAD, but the method based on Discrete Wavelet Transform is also one of the few multidimensional approaches in the field. The paper addresses the performance of both methods with clean speech and speech in noisy conditions, and discusses their limitations.