publication . Preprint . 2009

Inter Genre Similarity Modelling For Automatic Music Genre Classification

Bagci, Ulas; Erzin, Engin;

Open Access English

Published: 18 Jul 2009

Summary
references
8

Abstract

Music genre classification is an essential tool for music information retrieval systems and it has been finding critical applications in various media platforms. Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates inter-genre similarity modelling (IGS) to improve the performance of automatic music genre classification. Inter-genre similarity information is extracted over the mis-classified feature population. Once the inter-genre similarity is modelled, elimination of the inter-genre similarity reduces the inter-genre confusion and improves the identification rates. Inter-genre ...

Subjects

ACM Computing Classification System: ComputingMilieux_PERSONALCOMPUTING

free text keywords: Computer Science - Sound, Computer Science - Artificial Intelligence, Statistics - Machine Learning, H.5.5, I.5

Download from

arXiv.org e-Print Archive

Preprint . 2009

Provider: arXiv.org e-Print Archive

[1] G. Tzanetakis and P. Cook, “Musical genre classification of audio signals,” Speech and Audio Processing, IEEE Transactions on, vol. 10, no. 5, pp. 293-302, July 2002.

[2] D. Pye, “Content-based methods for managing electronic music,” in Proc. of the Int. Conf. on Acoustics, Speech and Signal Processing 2000 (ICASSP 2000), 2000, pp. 2437 - 2440.

[3] T. Li, M. Ogihara, and Q. Li, “A comparative study on content-based music genre classification,” in Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, 2003, pp. 282-289.

[4] S. Lippens, J.P. Martens, T. De Mulder, and G. Tzanetakis, “A comparison of human and automatic musical genre classification,” in Proc. of the Int. Conf. on Acoustics, Speech and Signal Processing 2004 (ICASSP 2004), May 2004, vol. 4, pp. 233-236.

[5] D. Perrot and R. Gjerdigen, “Scanning the dial: An exploration of factors in identification of musical style,” in Proc. Soc. Music Perception Cognition, 1999, p. 88.

[6] B. Logan, “Mel frequency cepstral coefficients for music modeling,” in Proc. Int. Symposium on Music Information Retrieval, ISMIR, 1997, pp. 138-147.

[7] S. Ravindran and D.V. Anderson, “Boosting as a dimensionality reduction tool for audio classification,” in Circuits and Systems, 2004. ISCAS '04. Proceedings of the 2004 International Symposium on, May 2004, vol. 3, pp. 465-468.

[8] U. Ba g˘cı and E. Erzin, “Boosting classifiers for music genre classification,” in 20th International Symposium on Computer and Information Sciences (ISCIS 2005) and also appeared in Lecture Notes in Computer Science (LNCS) by Springer-Verlag, October 2005, pp. 575-584.

Any information missing or wrong?Report an Issue