Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Benetos, Emmanouil; Kotropoulos, Costas (2008)
Types: Conference object
Subjects: M1, QA75

Classified by OpenAIRE into

arxiv: Computer Science::Sound
ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
Most music genre classification techniques employ pattern recognition algorithms to classify feature vectors extracted from recordings into genres. An automatic music genre classification system using tensor representations is proposed, where each recording is represented by a feature matrix over time. Thus, a feature tensor is created by concatenating the feature matrices associated to the recordings. A novel algorithm for non-negative tensor factorization (NTF), which employs the Frobenius norm between an n-dimensional raw feature tensor and its decomposition into a sum of elementary rank-1 tensors, is developed. Moreover, a supervised NTF classifier is proposed. A variety of sound description features are extracted from recordings from the GTZAN dataset, covering 10 genre classes. NTF classifier performance is compared against multilayer perceptrons, support vector machines, and non-negative matrix factorization classifiers. On average, genre classification accuracy equal to 75% with a standard deviation of 1% is achieved. It is demonstrated that NTF classifiers outperform matrix-based ones.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] T. Lidy and A. Rauber, “Evaluation of feature extractors and psycho-acoustic transformations for music genre classification,” in Proc. 6th Int. Conf. Music Information Retrieval, pp. 34-41, September 2005.
    • [2] M. I. Mandel, G. E. Poliner, and D. P. W. Ellis, “Support vector machine active learning for music retrieval,” Multimedia Systems, vol. 12, no. 1, pp. 3-13, 2006.
    • [3] L. De Lathauwer, “Signal Processing Based on Multilinear Algebra”, Ph.D. Thesis, K.U. Leuven, E.E. Dept.- ESAT, Belgium, 1997.
    • [4] MPEG-7, “Information Technology-Multimedia Content Description Interface-Part 4: Audio,” ISO/IEC JTC1/SC29/WG11 N5525, March 2003.
    • [5] F. van der Hedjen, R. P. W. Duin, D. de Ridder, and D. M. J. Tax, Classification, Parameter Estimation and State Estimation, London UK: Wiley, 2004.
    • [6] L. M. Bregman, “The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming,” USSR Computational Mathematics and Mathematical Physics, Vol. 7, pp. 200-217, 1967.
    • [7] S. Sra and I. S. Dhillon, “Nonnegative matrix approximation: algorithms and applications,” Technical Report TR-06-27, Computer Sciences, University of Texas at Austin, 2006.
    • [8] G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” IEEE Trans. Speech and Audio Processing, Vol. 10, No. 5, pp. 293-302, July 2002.
    • [9] T. Li, M. Ogihara, and Q. Li, “A comparative study on content-based music genre classification,” in Proc. 26th Annual ACM Conf. Research and Development in Information Retrieval, pp. 282-289, July-August 2003.
    • [10] E. Pampalk, A. Flexer, and G. Widmer, “Improvements of audio based music similarity and genre classification,” in Proc. 6th Int. Symp. Music Information Retrieval, pp. 628-633, 2005.
    • [11] J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. Kegl, “Aggregate features and AdaBoost for music classification,” Machine Learning, vol. 65, nos 2-3, pp. 473- 484, 2006.
    • [12] D. D. Lee and H. S. Seung, “Algorithms for nonnegative matrix factorization,” Advances in Neural Information Processing Systems, Vol. 13, pp. 556-562, 2001.
    • [13] S. Z. Li, X. Hou, H. Zhang, and Q. Cheng, “Learning spatially localized, parts-based representation,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-6, 2001.
    • [14] C. Hu, B. Zhang, S. Yan, Q. Yang, J. Yan, Z. Chen, and W. Ma, “Mining ratio rules via principal sparse non-negative matrix factorization,” in Proc. 2004 IEEE Int. Conf. Data Mining, pp. 407-410, November 2004.
    • [15] A. Shashua and T. Hazan, “Non-negative tensor factorization with applications to statistics and computer vision,” in Proc. 22nd Int. Conf. Machine Learning, pp. 792-799, August 2005.
    • [16] M. Welling and M. Weber, “Positive Tensor Factorization,” Pattern Recognition Letters, vol. 22, pp. 1255- 1261, 2001.
    • [17] M. Heiler and C. Schn¨orr, “Controlling sparseness in non-negative tensor factorization,” in Proc. 9th European Conf. Computer Vision, Vol. 1, pp. 56-67, May 2006.
    • [18] D. FitzGerald, M. Cranitch, and F. Coyle, “Sound source separation using shifted non-negative tensor factorization,” in Proc. 2006 IEEE Int. Conf. Acoustics, Speech, and Signal Processing, vol. V, pp. 653-658, May 2006.
    • [19] A. Cichocki, R. Zdunek, S. Choi, R. Plemmons, and S. Amari, “Non-negative tensor factorization using alpha and beta divergences,” in Proc. 2007 IEEE Int. Conf. Acoustics, Speech, and Signal Processing, April 2007.
    • [20] T. Kolda and B. Bader, “Matlab tensor classes”, SAND2004-589, http://csmr.ca.sandia.gov/tgkolda
    • [21] A. Cichocki, R. Zdunek, and S. Amari,“Nonnegative matrix and tensor factorization,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp. 142-145, January 2008.
    • [22] E. Benetos, M. Kotti, and C. Kotropoulos, “Large scale musical instrument identification,” in Proc. 4th Sound and Music Computing Conference, July 2007.
    • [23] I. Guyon, J. Makhoul, R. Schwartz, and V. Vapnik, “What size test set gives good error rate estimates?,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 52-64, January 1998.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article