Using Digraphs and a Second-Order Markovian Model for Rhythm Classification
The constant increase of online music data has required reliable and faster tools for retrieval and classification of music content. In this scenario, music genres provide interesting descriptors, since they have been used for years to organize music collections and can summarize common patterns in music pieces. In this paper we extend a previous work by considering digraphs and a second-order Markov chain to model rhythmic patterns. Second-order transition probability matrices are obtained, reflecting the temporal sequence of rhythmic notation events. Additional features are also incorporated, complementing the creation of an effective framework for automatic classification of music genres. Feature extraction is performed by principal component analysis and linear discriminant analysis techniques, whereas the Bayesian classifier is used for supervised classification. We compare the obtained results with those obtained by using a previous approach, where a first-order Markov chain had been used.Quantitative results obtained by the kappa coefficient corroborate the viability and superior performance of the proposed methodology. We also present a complex network of the studied music genres.
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- 1.Akhtaruzzaman, M.: Representation of musical rhythm and its classification system based on mathematical and geometrical analysis. In: Proceedings of the International Conference on Computer and Communication Engineering, pp. 466–471 (2008)Google Scholar
- 2.Blondel, V.D., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment (10), 10,008 (2008)Google Scholar
- 3.Cataltepe, Z., Yasian, Y., Sonmez, A.: Music genre classification using midi and audio features. EURASIP Journal on Advances of Signal Processing, 1–8 (2007)Google Scholar
- 6.Correa, D.C., Costa, L.d.F., Saito, J.H.: Musical genres: Beating to the rhythms of different drums. New Journal of Physics 12(053030), 1–38 (2010)Google Scholar
- 9.Li, T., Ogihara, M., Shao, B., Wang, D.: Machine Learning Approaches for Music Information Retrieval. In: Theory and Novel Applications of Machine Learning, pp. 259–278. I-Tech, Vienna (2009)Google Scholar
- 10.Miranda, E.R.: Composing Music With Computers. Focal Press (2001)Google Scholar
- 11.Mostafa, M.M., Billor, N.: Recognition of western style musical genres using machine learning techniques. Expert Systems with Applications 36, 11,378–11,389 (2009)Google Scholar
- 12.Pachet, F., Cazaly, D.: A taxonomy of musical genres. In: Proc. Content-Based Multimedia Information Acess (RIAO) (2000)Google Scholar
- 13.Scaringella, N., Zoia, G., Mlynek, D.: Automatic genre classification of music content - a survey. IEEE Signal Processing Magazine, 133–141 (2006)Google Scholar