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Vectors of “features 1” of silence frames representing 4 noise classes

Voice activity detection algorithm using spectral-correlation and wavelet-packet transformation

O. Korniienko, E. Machusky

Abstract


It is developed the voice activity detection algorithm using noise classification technique. It is proposed the spectral-correlation and wavelet-packet (WP) features of frames for voice activity estimation. There are tested three WP trees for effective representing of audio segments: mel-scaled wavelet packet tree, bark-scaled wavelet packet tree and ERB-scaled (equivalent rectangular bandwidth) wavelet packet tree. Application only two principal components of WP features allows to classify accurately the environment noise. The using wavelet-packet tree design which follows the concept of equivalent rectangular bandwidth for acoustic feature extraction allows to increase the voice/silence segments classification accuracy by at least 4% in compare to other classification based voice activity detection algorithms for different noise.

Keywords


voice activity detection; correlation analysis; wavelet-packet analysis; critical band; wavelet-packet cepstral coefficients

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References


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DOI: https://doi.org/10.3103/S0735272718050011

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