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Fine-tuning SVM for Enhancing Speech/Music Classification  

Lim, Chung-Soo (Dep. of Electronics Engineering, Inha University)
Song, Ji-Hyun (Dep. of Electronics Engineering, Inha University)
Chang, Joon-Hyuk (Dep. of Electronic Engineering, Hanyang University)
Publication Information
Abstract
Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.
Keywords
Support Vector Machine (SVM); Selectable Mode Vocoder (SMV); Kernel; Speech/Music Classification Algorithm;
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1 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, vol. 1.0, April. 2005.
2 Y. Gao, E. Shlomot, A. Benyassine, J. Hyssen, Huan-yu Su, and C. Murgia, "The SMV algorithm selected by TIA and 3GPP2 for CDMA appications," in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 709-712, May 2001.
3 S. -K. Kim and J. -H. Chang, "Speech/music classification enhancement for 3GPP2 SMV codec based on support vector machine," IEICE Trans. Fundamentals of Electronics, Communications and Computer Sciences, Vol. E92-A, no. 2, February 2009.
4 X. Wang, J. Chen, P Wang, Z. Huang, "Infrared human face auto locating based on SVM and a smart thermal biometrics system," in Proc. Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) , vol. 2, pp. 1066-1072, October 2006.
5 A. Ganapathiraju, J. E. Hamaker, J. Picone, "Applications of support vector machines to speech recognition," IEEE Trans. Signal Processing, vol. 52, pp. 2348-2355, August 2004.   DOI   ScienceOn
6 L. -P. Bi, H. Huang, Z. -Y. Zheng, and H. -T. Song, "New heuristic for determination Gaussian kernel's parameter," in Proc. International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4299-4304, August 2005.
7 S. S. Keerthi, C. -J. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural Computation, vol. 15, pp. 1667-1689, 2003.   DOI   ScienceOn
8 J. Tian and L. Zhao, "Weighted Gaussian kernel with multiple widths and network kernel pattern," in Proc. International Symposium on Information Engineering and Electronic Commerce, pp. 379-382, May 2009.
9 N. E. Ayat, M. Cheriet, and C. Y. Suen, "Automatic model selection for the optimization of SVM kernel," Pattern Recognition, vol. 38, pp. 1733-1745, October 2005.   DOI   ScienceOn
10 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 of Electronics, Communications and Computer Sciences, vol. E93-A, no. 1, pp. 316-319, January 2010.   DOI   ScienceOn
11 S. C. Greer, and A. Dejaco, "Standardization of the selectable mode vocoder," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 953-956, May 2001.
12 C. V. Goudar, P. Rabha, M. Deshpande, and A. Rao, "SMVLite: reduced complexity selectable mode vocoder," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 701-704, May 2006.
13 W. M. Fisher, G. R. Doddington and K. M. Goudie-Marshall, "The DARPA speech recognition research database: Specifications and status," in Proc. DARPA Workshop Speech Recognition, pp. 93-99, February 1986.