Speech/Music Classification Enhancement for 3GPP2 SMV Codec Based on Deep Belief Networks

Author:

SONG Ji-Hyun1,AN Hong-Sub1,LEE Sangmin1

Affiliation:

1. Department of Electronic Engineering, Inha University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Reference11 articles.

1. [1] Y. Gao, E. Shlomot, A. Benyassine, J. Thyssen, H. Su, and C. Murgia, “The SMV algorithm selected by TIA and 3GPP2 for CDMA applications,” Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp.709-712, May 2001.

2. [2] 3GPP2 Spec., “Source-controlled variable-rate multimedia wideband speech codec (VMR-WB), service option 62 and 63 for spread spectrum systems,” 3GPP2-C.S0052-A, v.1.0, April 2005.

3. [3] S.K. Kim and J.-H. Chang, “Discriminative weight training for support vector machine-based speech/music classification in 3GPP2 SMV codec,” IEICE Trans. Fundamentals, vol.E93-A, no.1, pp.316-319, Jan. 2010.

4. [4] C. Lim and J.-H. Chang, “Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion,” IET Signal Process., vol.6, no.4, pp.335-340, Jan. 2012.

5. [5] J. Wu and X.L. Zhang, “Maximum margin clustering based statistical VAD with multiple observation compound feature,” IEEE Signal Process. Lett., vol.18, no.5, pp.283-286, 2011.

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