Audio Classification and Categorization Based on Wavelets and Support Vector Machine

IEEE Transactions on Speech and Audio Processing

Volumn 13, Issue 5, 2005, Pages 644-651

  Lin, Chien Chang ^a   Chen, Shi Huang ^b   Truong, Trieu Kien ^a   Chang, Yukon ^a  

^a I SHOU UNIVERSITY   (Taiwan)

^b SHU TE UNIVERSITY   (Taiwan)

Author keywords

Audio categorization; audio classification; support vector machine (SVM); wavelets

Indexed keywords

EID: 85008016199     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSA.2005.851880     Document Type: Article

References (21)

1. E. Wold, T. Blum, D. Keislar, and J. Wheaton, “Content-based classification, search and retrieval of audio,” IEEE Multimedia, vol. 3, no. 3, pp. 27–36, Jul. 1996.
2. S. Z. Li, “Content-based audio classification and retrieval using the nearest feature line method,” IEEE Trans. Speech Audio Process., vol. 8, no. 5, pp. 619–625, Sep. 2000.
3. T. Zhang and C.-C.J. Kuo, “Audio content analysis for online audio-visual data segmentation and classification,” IEEE Trans. Speech Audio Process., vol. 9, no. 4, pp. 441–457, May 2001.
4. L. Lu, H.-J. Zhang, and H. Jiang, “Content analysis for audio classification and segmentation,” IEEE Trans. Speech Audio Process., vol. 10, no. 7, pp. 504–516, Oct. 2002.
5. G. Guo and S. Z. Li, “Content-based audio classification and retrieval by support vector machines,” IEEE Trans. Neural Networks, vol. 14, no. 1, pp. 209-215, Jan. 2003.
6. F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778–1790, Aug. 2004.
7. P. Clarkson and P. J. Moreno, “On the use of support vector machines for phonetic classification,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process., vol. 2, Mar. 1999, pp. 585–588.
8. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
9. V. Kecman, Learning and Soft Computing. Cambridge, MA: MIT Press, 2001.
10. P. Ding, Z. Chen, Y. Liu, and B. Xu, “Asymmetrical support vector machines and applications in speech processing,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process., vol. 1, May 2002, pp. 1-73-1-76.
11. A. Ganapathiraju, J. E. Hamaker, and J. Picone, “Applications of support vector machines to speech recognition,” IEEE Trans. Signal Process., vol. 52, no. 8, pp. 2348–2355, Aug. 2004.
12. F. Schwenker, “Hierarchical support vector machines for multi-class pattern recognition,” in Proc. IEEE Fourth Int. Conf. Knowledge-Based Intelligent Eng. Syst. Allied Technologies, vol. 2, Sep. 2000, pp. 561–565.
13. T. Luo, K. Kramer, D. B. Goldgof, L. O. Hall, S. Samson, A. Remsen, and T. Hopkins, “Recognizing plankton images from the shadow image particle profiling evaluation recorder,” IEEE Trans. Syst., Man Cybern. —B: Cybern., vol. 34, no. 4, pp. 1753–1762, Aug. 2004.
14. H. Fenglei and W. Bingxi, “Text-independent speaker recognition using support vector machine,” in Proc. Int. Conf. Info-Tech Info-Net, vol. 3, Nov. 2001, pp. 402–407.
15. M. Pontil and A. Verri, “Support vector machines for 3-D object recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 6, pp. 637–646, Jun. 1998.
16. S. Mallat, A Wavelet Tour of Signal Processing. New York: Academic, 1998.
17. C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms. Englewood Cliffs, NJ: Prentice-Hall, 1998.
18. S.-H. Chen and J.-F. Wang, “Noise-robust pitch detection method using wavelet transform with aliasing compensation,” Proc. Inst. Elect. Eng. Vision, Image Signal Process., vol. 149, no. 6, pp. 327–334, Dec. 2002.
19. C.-T. Hsieh, E. Lai, and Y.-C. Wang, “Robust speech features based on wavelet transform with application to speaker identification,” Proc. Inst. Elect. Eng. Vision, Image Signal Process., vol. 149, no. 2, pp. 108–114, Apr. 2002.
20. W. C. Chu, Speech Coding Algorithms. New York: Wiley, 2003.
21. http://www.musclefish.com/cbrdemo.html [Online]