Classificação de Gêneros Musicais por Texturas no Espaço de Frequência
Resumo
Este artigo descreve a classificação de gêneros musicais utilizando espectrogramas extraídos de músicas. Foram gerados espectrogramas a partir do sinal de áudio das músicas, tomadas da “Latin Music Database”. A partir destes espectrogramas foram extraídas características de textura com o uso de matriz de co-ocorrência. Foi utilizado Support Vector Machine para a classificação e o desempenho final do esquema de classificação proposto atingiu uma taxa média de acerto de 60,11%. Este desempenho é superior ao de outras abordagens recentemente aplicadas sobre a mesma base. O classificador aqui proposto foi ainda combinado com um outro classificador descrito na literatura. A combinação atingiu taxa de acerto de 66,11%, com limite superior de 75%.
Referências
Chang, C.-C. and Lin, C.-J. (2001). LIBSVM: a library for support vector machines. Software available at [link].
Costa, C., Valle Jr, J., and Koerich, A. (2004). Automatic classification of audio data. In IEEE International Conference on Systems, Man, and Cybernetics, pages 562–567.
French, M. and Handy, R. (2007). Spectrograms: turning signals into pictures. Journal of engineering technology, 24(1):p. 32–35.
Gantz, J., Chute, C., Manfrediz, A., Minton, S., Reinsel, D., Schlichting, W., and Toncheva, A. (2008). The diverse and exploding digital universe: An updated forecast of worldwide information growth through 2011. IDC white paper, sponsored by EMC.
Haralick, R., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on systems, man and cybernetics, 3(6):p. 610–621.
Kittler, J., Hatef, M., Duin, R., and Matas, J. (2002). On combining classifiers. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(3):p. 226–239.
Koerich, A. and Poitevin, C. (2005). Combination of Homogeneous Classifiers for Musical Genre Classification. IEEE International Conference on Systems, Man, and Cybernetics, pages 554–559.
Li, T., Ogihara, M., and Li, Q. (2003). A comparative study on content-based music genre classification. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 282–289. ACM.
Lopes, M., Gouyon, F., Koerich, A. L., and Oliveira, L. E. S. (2010). Selection of training instances for music genre classification. ICPR 2010 - 20th International Conference on Pattern Recognition.
Lucena, C., Medeiros, C., Lucchesi, C., Maldonado, J., Almeida, V., and outros (2006). Grandes Desafios da Pesquisa em Computação no Brasil - 2006-2016. Relatório sobre o seminário Grandes Desafios da Pesquisa em Computação. São Paulo. SBC/CAPES/FAPESP.
Pampalk, E., Flexer, A., and Widmer, G. (2005). Improvements of audio-based music similarity and genre classification. In proc. ISMIR, volume 5.
Silla Jr, C., Koerich, A., and Kaestner, C. (2008). The latin music database. In Proceedings of the 9th International Conference on Music Information Retrieval, pages 451–456.
Tzanetakis, G. and Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5):p. 293–302.
Vapnik, V. (2000). The nature of statistical learning theory. Springer Verlag.
Yu, G. and Slotine, J. (2009). Audio classification from time-frequency texture. ICASSP 2009 - International Conference on Acoustics, Speech and Signal Processing, pages 1677–1680.
Costa, C., Valle Jr, J., and Koerich, A. (2004). Automatic classification of audio data. In IEEE International Conference on Systems, Man, and Cybernetics, pages 562–567.
French, M. and Handy, R. (2007). Spectrograms: turning signals into pictures. Journal of engineering technology, 24(1):p. 32–35.
Gantz, J., Chute, C., Manfrediz, A., Minton, S., Reinsel, D., Schlichting, W., and Toncheva, A. (2008). The diverse and exploding digital universe: An updated forecast of worldwide information growth through 2011. IDC white paper, sponsored by EMC.
Haralick, R., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on systems, man and cybernetics, 3(6):p. 610–621.
Kittler, J., Hatef, M., Duin, R., and Matas, J. (2002). On combining classifiers. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 20(3):p. 226–239.
Koerich, A. and Poitevin, C. (2005). Combination of Homogeneous Classifiers for Musical Genre Classification. IEEE International Conference on Systems, Man, and Cybernetics, pages 554–559.
Li, T., Ogihara, M., and Li, Q. (2003). A comparative study on content-based music genre classification. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 282–289. ACM.
Lopes, M., Gouyon, F., Koerich, A. L., and Oliveira, L. E. S. (2010). Selection of training instances for music genre classification. ICPR 2010 - 20th International Conference on Pattern Recognition.
Lucena, C., Medeiros, C., Lucchesi, C., Maldonado, J., Almeida, V., and outros (2006). Grandes Desafios da Pesquisa em Computação no Brasil - 2006-2016. Relatório sobre o seminário Grandes Desafios da Pesquisa em Computação. São Paulo. SBC/CAPES/FAPESP.
Pampalk, E., Flexer, A., and Widmer, G. (2005). Improvements of audio-based music similarity and genre classification. In proc. ISMIR, volume 5.
Silla Jr, C., Koerich, A., and Kaestner, C. (2008). The latin music database. In Proceedings of the 9th International Conference on Music Information Retrieval, pages 451–456.
Tzanetakis, G. and Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on speech and audio processing, 10(5):p. 293–302.
Vapnik, V. (2000). The nature of statistical learning theory. Springer Verlag.
Yu, G. and Slotine, J. (2009). Audio classification from time-frequency texture. ICASSP 2009 - International Conference on Acoustics, Speech and Signal Processing, pages 1677–1680.
Publicado
19/07/2011
Como Citar
COSTA, Yandre M. G.; OLIVEIRA, Luiz S.; KOERICH, Alessandro L.; GOUYON, Fabien.
Classificação de Gêneros Musicais por Texturas no Espaço de Frequência. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 38. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 1352-1365.
ISSN 2595-6205.
