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http://dx.doi.org/10.9717/kmms.2014.17.9.1064

Voice Activity Detection based on Adaptive Band-Partitioning using the Likelihood Ratio  

Kim, Sang-Kyun (Division of Electronic Engineering, Inha Univ.)
Shim, Hyeon-Min (Division of Electronic Engineering, Inha Univ.)
Lee, Sangmin (Division of Electronic Engineering, Inha Univ.)
Publication Information
Journal of Korea Multimedia Society / v.17, no.9, 2014 , pp. 1064-1069 More about this Journal
Abstract
In this paper, we propose a novel approach to improve the performance of a voice activity detection(VAD) which is based on the adaptive band-partitioning with the likelihood ratio(LR). The previous method based on the adaptive band-partitioning use the weights that are derived from the variance of the spectral. In our VAD algorithm, the weights are derived from LR, and then the weights are incorporated with the entropy. The proposed algorithm discriminates the voice activity by comparing the weighted entropy with the adaptive threshold. Experimental results show that the proposed algorithm yields better results compared to the conventional VAD algorithms. Especially, the proposed algorithm shows superior improvement in non-stationary noise environments.
Keywords
Voice Activity Detection; Adaptive Band-Partitioning; Likelihood Ratio;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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