As a result of recent technological innovations, there has been a tremendous growth in the Electronic Music Distribution industry. In this way, tasks such us automatic music genre classification address new and exciting research challenges. Automatic music genre recognition involves issues like feature extraction and development of classifiers using the obtained features. As for feature extraction, we use features such as the number of zero crossings, loudness, spectral centroid, bandwidth and uniformity. These are statistically manipulated, making a total of 40 features. As for the task of genre modeling, we train a feedforward neural network (FFNN). A taxonomy of subgenres of classical music is used. We consider three classification problems: in the first one, we aim at discriminating between music for flute, piano and violin; in the second problem, we distinguish choral music from opera; finally, in the third one, we aim at discriminating between all five genres. Preliminary results are presented and discussed, which show that the presented methodology may be a good starting point for addressing more challenging tasks, such as using a broader range of musical categories.
Keywords
Neural networks, music classification, music signal processing, music information retrieval
Subject
Music Classification
Conference
ASC'2004, September 2004
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Cited by
Year 2012 : 1 citations
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Year 2008 : 2 citations
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Year 2007 : 1 citations
1. Alluri, V. (2007). “TOWARD AUTOMATIC MUSICOLOGICAL CLASSIFICATION OF WESTERN CLASSICAL MUSIC”. MSc Thesis, University of Miami, USA.
Year 2006 : 2 citations
Ezzaidi H. and Rouat J. (2006). “Automatic Musical Genre Classification Using Divergence and Average Information Measures”. International Journal of Applied Mathematics and Computer Sciences, Vol. 3, No. 4, pp. 202 – 206.
Xin Jin, Rongfang Bie. Random Forest and PCA for Self-Organizing Maps based Automatic Music Genre Discrimination. In Proceedings of the International Conference on Data Mining - DMIN'2006, pp.414-417.
Year 2005 : 1 citations
Kordos, M., Search-based algorithms for multilayer perceptrons. Tese de Doutoramento, Silesian University of Technology, Polónia, 2005.
