T.V. Pham, M. Képesi, G. Kubin, L. Weruaga (Austria), M. Sigmund, and T. Dostál (Czech Republic)
Chirp analysis, Wavelet decomposition, voice activity detection, phonetic classification.
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 multidi mensional approaches in the field. The paper addresses the performance of both methods with clean speech and speech in noisy conditions, and discusses their limitations.
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