Voice Activity Detection Algorithm Using Spectral-Correlation and Wavelet-Packet Transformation
- 作者: Korniienko O.1, Machusky E.1
-
隶属关系:
- National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
- 期: 卷 61, 编号 5 (2018)
- 页面: 185-193
- 栏目: Article
- URL: https://journals.rcsi.science/0735-2727/article/view/177205
- DOI: https://doi.org/10.3103/S0735272718050011
- ID: 177205
如何引用文章
详细
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.
作者简介
O. Korniienko
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
编辑信件的主要联系方式.
Email: olexandr.korniienko@gmail.com
乌克兰, Kyiv
E. Machusky
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”
Email: olexandr.korniienko@gmail.com
乌克兰, Kyiv