Browse > Article
http://dx.doi.org/10.7776/ASK.2009.28.5.471

Analysis and Implementation of Speech/Music Classification for 3GPP2 SMV Codec Employing SVM Based on Discriminative Weight Training  

Kim, Sang-Kyun (인하대학교 전자공학부)
Chang, Joon-Hyuk (인하대학교 전자공학부)
Cho, Ki-Ho (서울대학교 전기컴퓨터공학부)
Kim, Nam-Soo (서울대학교 전기컴퓨터공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.28, no.5, 2009 , pp. 471-476 More about this Journal
Abstract
In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.
Keywords
Speech/Music Classification Algorithm; Selectable Mode Vocoder (SMV); Support Vector Machine (SVM); Minimum Classification Error; Discriminative Weight Training;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 3GPP2 Spec., "Selectable Mode Vocoder (SW) Service Option forr Wideband Spread Spectrum Communication Systems," 3GPP2-C.S0030-0, v3.0, Jan. 2004
2 N. Cristianini and J. Shawe-Taylor, An introduction to su-pport vector machines and other kernel-based learning methods. Cambridge Univ. Press, 2000
3 B.-H. Juang. W. Chou and C.-H. Lee, "Minimum classifi-cation error rate methods for speech recognition," IEEE Trans. Speech Audio Processing, vol. 5, no. 3, pp. 257-265, 1997   DOI   ScienceOn
4 김상균, 장준혁, "SMV코덱의 음성/음악 분류 성능 향상을 위한 Support Vector Machine의 적용" , 전자공학회논문지, 제45권, 제6호, pp.142-147, 2008   과학기술학회마을
5 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, Apr. 2005
6 V. N. Vapnik, "An overview of statistical learning theory," IEEE Trans. Neural Networks, vol. 10, no. 5, pp. 988-999, 1999   DOI   ScienceOn
7 J. Saunders, "Real-time discrimination of broadcast speech/music," Proc. IEEE International Conference on Acoustics, Speech, and Processing, vol. 2, pp. 993-996, May 1996   DOI
8 W. Q. Wang, W. Gao arid D. W. Ying, "A fast and robust speech/music Discrimination Approach," Proc. International Conference on Information, Communications and Singnal Pro-cessing, vol. 3, pp. 1325-1329, Dec. 2003   DOI
9 Y. Gao, E. Shlomot, A. Benyassine, J. Thyssen, Huan-yu Su and C. Murgia, "The SMV Algorithm Selected by TIA and 3GPP2 for CDMA Apphcations," Proc. IEEE International Con-ference on Acoustics, Speech and Siginal Processing, vol. 2, pp. 709-712, May 2001   DOI
10 S.-l. Kang, Q.-H. Jo and J.-H, Chang, "Discriminative weight training for a statistical model-based voice activity detection," IEEE SignaI Processing Letter, vol. 15, pp. 170-173, 2008   DOI   ScienceOn