Voice Activity Detection Algorithm Using Spectral-Correlation and Wavelet-Packet Transformation
- Authors: Korniienko O.1, Machusky E.1
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Affiliations:
- National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
- Issue: Vol 61, No 5 (2018)
- Pages: 185-193
- Section: Article
- URL: https://journals.rcsi.science/0735-2727/article/view/177205
- DOI: https://doi.org/10.3103/S0735272718050011
- ID: 177205
Cite item
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.
About the authors
O. Korniienko
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Author for correspondence.
Email: olexandr.korniienko@gmail.com
Ukraine, Kyiv
E. Machusky
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Email: olexandr.korniienko@gmail.com
Ukraine, Kyiv