Classification of audio signals using Fuzzy c-Means with divergence-based Kernel

Pattern Recognition Letters

Volumn 30, Issue 9, 2009, Pages 794-798

  Park, Dong Chul ^a  

^a Myong Ji University   (South Korea)

Author keywords

Divergence kernel; Fuzzy c Means; Gaussian probability density function (GPDF); Music classification; Self organizing map

Indexed keywords

AUDIO DATA; AUDIO RETRIEVAL; AUDIO SIGNAL; CLASSIFICATION ACCURACY; CLASSIFICATION METHODS; CONTENT-BASED; CONVENTIONAL ALGORITHMS; DATA SETS; DATA TRANSFORMATION; DIVERGENCE DISTANCE; DIVERGENCE KERNEL; FEATURE DATA; FEATURE SPACE; FUZZY C MEAN; FUZZY C-MEANS; FUZZY C-MEANS ALGORITHMS; GAUSSIAN PROBABILITY DENSITY FUNCTION (GPDF); GAUSSIAN PROBABILITY DENSITY FUNCTIONS; INPUT SPACE; KERNEL METHODS; MUSIC CLASSIFICATION; NONLINEAR PROBLEMS; SIMILARITY MEASURE;

CLUSTERING ALGORITHMS; CONFORMAL MAPPING; COPYING; FUZZY CLUSTERING; FUZZY RULES; FUZZY SYSTEMS; INFORMATION RETRIEVAL; LINEARIZATION; PROBABILITY; SIGNAL ANALYSIS;

PROBABILITY DENSITY FUNCTION;

EID: 67349269594     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2008.05.019     Document Type: Article

References (19)

1. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum, New York
2. Burred, J.J., Lerch, A., 2003. A hierachical approach to automatic music genre classification. In: Proc. 6th Internat. Conf. on Digital Audio Effect.
3. Chen J.H., and Chen C.S. Fuzzy kernel Perceptron. IEEE Trans. Neural Networks 13 6 (2002) 1364-1373
4. Chen S., and Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Systems Man Cybernet. 34 4 (2004) 1907-1916
5. Cover T.M. Geomeasural and statistical properties of systems of linear inequalities in pattern recognition. Electron. Comput. EC-14 (1965) 326-334
6. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machine (2000), Cambridge University Press, Cambridge
7. Foote, J., 1997. Content-based retrieval of music and audio. In: Proc. SPIE on Multimedia Storage and Archiving Systems II, pp. 138-147.
8. Girolami M. Mercer kernel-based clustering in feature space. IEEE Trans. Neural Networks 13 3 (2002) 780-784
9. p norm distances. IEEE Trans. Fuzzy Systems 8 5 (2000) 576-582
10. Li, G., Khokar, A., 2000. Content-based indexing and retrieval of audio data using wavelets. In: Proc. Internat. Conf. on Multimedia Expo II, pp. 885-888.
11. Liu, J.-X., Li, J., Tan., Y.-J., 2005. An empirical assessment on the robustness of support vector regression with different kernels. In: Proc. 4th Internat. Conf. on Machine Learning and Cybernetics, pp. 4289- 4294.
12. Lu, L., Li, S., Zhang, H., 2001. Content-based audio segmentation using support vector machines. In: Proc. IEEE Internat. Conf. on Multimedia and Expo (ICME), pp. 749-752.
13. Muller K.-R., Mika S., Ratsch G., Tsuda K., and Scholkopf B. An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 12 2 (2001) 181-201
14. Park D., Tran C.N., Min B., and Park S. Modeling and classification of audio signals using gradient-based Fuzzy c-Means algorithm with a Mercer kernel. LNAI 4099 (2006) 1104-1108
15. Saunders, J., 1996. Real time discrimination of broadcast speech/music. In: Proc. Internat. Conf. on Acoustic, Speech, Signal Processing (ICASSP), pp. 993-996.
16. Tran C.N., and Park D. MPEG video traffic modeling and classification using Fuzzy c-Means algorithm with divergence-based kernel. LNCS 4233 (2006) 439-447
17. Turnbull D., and Elkan C. Fast recognition of musical genres using RBF networks. IEEE Trans. Knowledge Data Eng. 17 4 (2005) 580-584
18. Tzanetakis G., and Cook P. Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10 5 (2005) 293-302
19. Wold E., Blum T., Keislar D., and Wheaton J. Content-based classification, search, and retrieval of audio. IEEE Trans. Multimedia 3 3 (1996) 27-36