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Multistage Feature-based Classification Model  

Song, Young-Soo (PANTECH Group, Japaneses Model Research Lab.)
Park, Dong-Chul (Dept. of Information Eng., Myong Ji University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.46, no.1, 2009 , pp. 121-127 More about this Journal
Abstract
The Multistage Feature-based Classification Model(MFCM) is proposed in this paper. MFCM does not use whole feature vectors extracted from the original data at once to classify each data, but use only groups related to each feature vector to classify separately. In the training stage, the contribution rate calculated from each feature vector group is drew throughout the accuracy of each feature vector group and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the contribution rate of each feature vector group. In this paper, the proposed MFCM algorithm is applied to the problem of music genre classification. The results demonstrate that the proposed MFCM outperforms conventional algorithms by 7% - 13% on average in terms of classification accuracy.
Keywords
Multistage feature; Classification model; Clustering algorithm; Music genre classification;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 K. Kim, S. Kim, J. Jeon, K. Park'Quick Audio Retrieval Using Multiple Feature Vectors,' IEEE Trans. Consumer Electronics. V. 52, No.1, 2006
2 J. Foote, 'Content-based retrieval of music and audio', Proc. SPIE, Multimedia Storage and Archiving Systems, pp 138-147. 1997
3 G. Li, and A. Khokar, 'Content-based indexing and retrieval of audio data using wavelets', Proc. Int. Conf. Multimedia Expo. pp 885-888. 2000
4 Y. Song and C. Zhang, 'Content-Based Information Fusion for Semi-Supervised Music Genre Classification,' IEEE Trans. Multimedia, V.10, No.1, pg. 145-152, 2007   DOI   ScienceOn
5 정성윤, 김민성, 손종목, 배건성, 'PCA-optimized 필터뱅크 기반의 MFCC 특징파라미터 추출 및 한국어 4연숫자 전화음성에 대한 인식실험,' 전자공학회논문지, 제41권 SP편, 6호, 279-2839쪽, 2004년 6월   과학기술학회마을   ScienceOn
6 J. C. Bezdek, 'A convergence theorem for the fuzzy ISODATA clustering algorithms', IEEE Trans. Pattern Anal. Mach. Int, Vol. 2. pp 1-8. 1980   DOI   ScienceOn
7 곽현욱, 오준택, 손영호, 김욱현, '엔트로피 기반의 가중치와 분포크기를 이용한 향상된 FCM 알고리즘,' 전자공학회논문지 제43권 SP편, 제4호, 1-8쪽, 2006년 4월   과학기술학회마을   ScienceOn
8 E. Wold, T. Blum, D., Keislar, and J. Wheaton, 'Content-based classification, search, and retrieval of audio', IEEE Trans. Multimedia, V.3, No.3, pp 27-36. 1996   DOI   ScienceOn
9 S. Okawa, and E. Bocchieri, A. Potamianos, 'Multi-band speech recognition in noisy environments', Proc. IEEE Int. Conf. ICASSP '98 Vol 2. pp 641-644. 1998
10 D.-C Park, and Y.-J. Woo, 'Weighted Centroid Neural Network for Edge Preserving Image Compression', IEEE Trans. Neural Networks, V.12, No. 5, pp 1134-1146. 2001   DOI   ScienceOn
11 D.-C Park et al., 'Centroid Neural Network with a Divergence Measure for GPDF Data Clustering', IEEE Trans. Neural Networks, V.19, No. 6, pp 948-957. 2008   DOI   ScienceOn
12 G. Tzanetakis, and P. Cook, 'Musical genre classification of audio signals', IEEE Trans. Speech Audio Process.V. 10, No.5, pp. 293-302, 2002   DOI   ScienceOn
13 Carl Looney, Pattern Recognition Using Neural Networks, Oxford University Press, New York, pp 252 -254, 1997
14 박찬응, '은닉 마코프 모델과 켑스트럴 계수들에 따른 한국어 속삭임의 인식 비교,' 전자공학회논문지, 제43권 IE편, 제2호, 22-29쪽, 2006년 2월   과학기술학회마을
15 T. Kohonen, 'The Self-Organizing Map', Proc. IEEE, Vol. 78. pp 1464-1480. 1990   DOI   ScienceOn
16 H. Christensen, B. Lindberg, and O. Andersen, 'Employing heterogeneous information in a multi-stream framework', Proc. IEEE Int. Conf. ICASSP '00 Vol 3. pp 1571-1574. 2000
17 N. Scaringella, G. Zoia, and D. Mlynek, 'Automatic genre classification of music content: a survey,' IEEE Signal. Proc. Mag., V. 23, No.2, pp. 133-141, 2006   DOI   ScienceOn