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Voice Activity Detection Algorithm base on Radial Basis Function Networks with Dual Threshold  

Kim Hong lk (한양대학교 전자통신전파공학과대학원 응용통신연구실)
Park Sung Kwon (한양대학교 전자전기컴퓨터공학부)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.29, no.12C, 2004 , pp. 1660-1668 More about this Journal
Abstract
This paper proposes a Voice Activity Detection (VAD) algorithm based on Radial Basis Function (RBF) network using dual threshold. The k-means clustering and Least Mean Square (LMS) algorithm are used to upade the RBF network to the underlying speech condition. The inputs for RBF are the three parameters in a Code Exited Linear Prediction (CELP) coder, which works stably under various background noise levels. Dual hangover threshold applies in BRF-VAD for reducing error, because threshold value has trade off effect in VAD decision. The experimental result show that the proposed VAD algorithm achieves better performance than G.729 Annex B at any noise level.
Keywords
voice activity detection; Radial Basis Function network; dual threshold;
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