Abstract
The problem of identifying sections of singer voice and instruments is investigated in this paper. Three classification techniques: Linde-Buzo-Gray algorithm (LBG), Gaussian Mixture Models (GMM) and feed-forward Multi-Layer Perception (MLP) are presented and compared in this paper. All techniques are based on Mel frequency Cepstral Coefficients (MFCC), which commonly used in the speech and speaker recognition domains. All the proposed approaches yield a decision at every 125 ms only. Particularly, a large experimental data is extracted from the music genre database RWC including various style (68 pieces, 25 subcategories). The recognition scores are evaluated on data used in the training session and others never seen by proposed systems. The best results are obtained with the GMM (94% with train data and 80.5% with test data).
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Mesaros, A., Virtanen, T., Klapuri, A.: Singer identification in polyphonic music using vocal separation and pattern recognition methods. In: Proc. ISMIR, Vienna, Austria (2007)
Tzanetaki, G., Essl, G., Cook, P.: Automatic musical genre classification of audio signals. In: Proc. ISMIR, Bloomington, Indiana (2001)
Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Comm. 28(1), 84–95 (1980)
Berenzweig, A., Ellis, D., Lawrence, S.: Using voice segments to improve artist classification of music. In: Proc. AES-22 Intl. Conf. on Virt., Synth., and Ent. Audio., Espoo, Finland (June 2002)
Kim, Y.E., Whitman, B.: Singer identification in popular music recordings using voice coding featuress. In: Proc. ISMIR, Paris, France (2002)
Goto, M., Hashiguchi, H., Nishimura, T., Oka, R.: Rwc music database: Music genre database and musical instrument sound database. In: Proc. ISMIR, pp. 229–230 (2003)
The auditory toolbox for matlab, http://cobweb.ecn.purdue.edu/~malcolm/interval/1998010/
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Ezzaidi, H., Bahoura, M. (2010). Statistical and Neural Classifiers: Application for Singer and Music Discrimination in Polyphonic Music Context. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D., Meunier, J. (eds) Image and Signal Processing. ICISP 2010. Lecture Notes in Computer Science, vol 6134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13681-8_16
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DOI: https://doi.org/10.1007/978-3-642-13681-8_16
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