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


Vol. 29,  No. 12, pp. 1660-1668, Dec.  2004


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  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.

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  Cite this article

[IEEE Style]

H. Kim and S. Park, "Voice Activity Detection Algorithm base on Radial Basis Function Networks with Dual Threshold," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 12, pp. 1660-1668, 2004. DOI: .

[ACM Style]

Hong-Ik Kim and Sung-Kwon Park. 2004. Voice Activity Detection Algorithm base on Radial Basis Function Networks with Dual Threshold. The Journal of Korean Institute of Communications and Information Sciences, 29, 12, (2004), 1660-1668. DOI: .

[KICS Style]

Hong-Ik Kim and Sung-Kwon Park, "Voice Activity Detection Algorithm base on Radial Basis Function Networks with Dual Threshold," The Journal of Korean Institute of Communications and Information Sciences, vol. 29, no. 12, pp. 1660-1668, 12. 2004.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880