Genre Classification of Traditional Malay Music Using Spectrogram Correlation

  • Abstract
  • Keywords
  • References
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  • Abstract

    A method to classify the genre of traditional Malay music using spectrogram correlation is described.  The method can be divided into three distinct parts consisting of spectrogram construction that retains the most salient feature of the music, template construction that takes into account the variations in music within a genre as well as the music progresses, and template matching based on spectrogram image cross-correlation with unconstrained minimum average correlation energy filters. Experiments conducted with seven genres of traditional Malay music show that the recognition accuracy is dependent on the number of segments used to construct the filter templates, which in turn is related to the length of music segment used. Despite using a small dataset, an average recognition rate of 61.8 percent was obtained for music segments lasting 180 seconds using six relatively short excerpts.



  • Keywords

    genre classification; traditional Malay music; spectrogram; unconstrained minimum average correlation energy filters.

  • References

      [1] Pálmason, H., Jónsson, B.Þ., Schedl, M., Knees, P. Music genre classification revisited: An in-depth examination guided by music experts. Proceedings of the International Symposium on Computer Music Multidisciplinary Research, 2017, pp. 45-56.

      [2] Nasuruddin M.G. The Malay traditional music. Dewan Bahasa dan Pustaka, 1992.

      [3] Tzanetakis, G., Cook, P. Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 2002, 10(5), 293-302.

      [4] Lee, C.H., Shih, J.L., Yu, K.M., Lin, H.S. Automatic music genre classification based on modulation spectral analysis of spectral and cepstral features. IEEE Transactions on Multimedia, 2009, 11(4), 670-682.

      [5] A. Nasridinov, Y.-H. Park. A study on music genre recognition and classification techniques. International Journal of Multimedia and Ubiquitous Engineering, 2014, 9(4), 31-42.

      [6] Geng, S., Ren, G., Ogihara, M. Transforming musical signals through a genre classifying convolutional neural network. Proceedings of the International Workshop on Deep Learning and Music, 2017, pp. 48-49.

      [7] Doraisamy, S., Golzari, S., Mohd, N., Sulaiman, M.N., Udzir, N.I. A study on feature selection and classification techniques for automatic genre classification of traditional Malay music. Proceedings of the International Society for Music Information Retrieval, 2008, pp. 331-336.

      [8] Norowi, N.M., Doraisiamy, S., Rahmat, R.W. Traditional Malaysian musical genres classification based on the analysis of beat feature in audio. Journal of Information Technology in Asia. Journal of Information Technology in Asia, 2007, 2(1), 95-109.

      [9] Yu D., Deng L. Automatic speech recognition. Springer London Limited, 2016.

      [10] McKinney, M.F., Breebaart, J. Features for audio and music classification. Proceedings of the International Conference on Music Information Retrieval, 2003, pp. 151-158.

      [11] Costa, Y.M., Oliveira, L.S., Koericb, A.L., Gouyon, F. Music genre recognition using spectrograms. Proceedings of the IEEE International Conference on Systems, Signals and Image Processing, 2011, pp. 1-4.

      [12] Costa, Y.M., Oliveira, L.S., Silla Jr, C.N. An evaluation of convolutional neural networks for music classification using spectrograms. Applied Soft Computing, 2017, 52, 28-38.

      [13] Kumar, B.V., Savvides, M., Xie, C., Venkataramani, K., Thornton, J., Mahalanobis, A. Biometric verification with correlation filters. Applied Optics, 2004, 43(2), 391-402.

      [14] Samad, S.A., Ramli, D.A., Hussain, A. Lower face verification centered on lips using correlation filters. Information Technology Journal, 2007, 6(8), 1146-1151.

      [15] Zhang, X., Zhu, B., Li, L., Li, W., Li, X., Wang, W., Lu, P., Zhang, W. SIFT-based local spectrogram image descriptor: A novel feature for robust music identification. EURASIP Journal on Audio, Speech, and Music Processing, 2015, 2015, 1-15.

      [16] Li, W., Liu, Y., Xue, X. Robust audio identification for MP3 popular music. Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010, pp. 627-634.

      [17] Yao S, Niu B, Liu J. Audio identification by sampling sub-fingerprints and counting matches. IEEE Transactions on Multimedia, 2017, 19(9), 1984-1995.




Article ID: 20683
DOI: 10.14419/ijet.v7i4.11.20683

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