Computer models of similarity in musical pieces based on the multi-dimensional patterns

• Autorzy: Mazurowski Ł. , Wiliński A.
• Języki publikacji: EN

Abstrakty

EN

This paper describes the problem of recognizing similarities in musical pieces in order to cluster and classify them with particular reference to the files stored according to the MIDI standard. The analysis of the similarity between artificially generated musical pieces to those that have been composed by a man which is carried out in order not to infringe copyrights to the existing pieces is the area of further use of the method presented. The article presents different existing methodological approaches and proposes the use of histograms of selected parameters of musical sound as a mechanism of aggregation of musical clusters potentially belonging to one group of similar musical pieces.

Słowa kluczowe

EN

artificial music; pattern recognition; knowledge discovery; data mining; MIDI

PL

sztuczna muzyka; rozpoznawanie wzorców; odkrywanie wiedzy; eksploracja danych; MIDI

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2012
• Tom: Vol. 20, nr 2
• Strony: 79--94
• Opis fizyczny: Bibliogr. 8 poz.

Twórcy

autor

Mazurowski Ł.

autor

Wiliński A.

• West Pomeranian University of Technology Szczecin, Faculty of Computer Science and Information Technology, Department of Multimedia Systems, Żołnierska 49, Szczecin 71-210, Poland,

Bibliografia

• [1] Mazurowski, L., The method of generating melodic contour based on the conception of Markov’s process, Master’s thesis, West Pomeranian University of Technology Szczecin, Poland, 2009, (in Polish).
• [2] Cheng-Che, L. and Tseng, S. V., A novel approach for music classification by extracting score features, International Journal of Innovative Computing, Information and Control, Vol. 5, No. 10, 2009, pp. 4725–4735.
• [3] Byrd, D. and Crawford, T., Problems of music information retrieval in the real world, Information Processing and Management, Vol. 38, 2002, pp. 249–272.
• [4] Casey, M. A. e. a., Content-Based Music Information Retrieval: Current Directions and Future Challenges, Proceedings of the IEEE, Vol. 96, No. 4, 2008, pp. 668–696.
• [5] Eerola, T. and Toiviainen, P., MIDI Toolbox: MATLAB Tools for Music Research, Department of Music, University of Jyväskylä, Finland, 2004, (in English).
• [6] Basili, R., Serafini, A., and Stellato, A., Classification of musical genre: a machine learning approach, Proceedings of the 5th International Conference on Music Information Retrieval, 2004.
• [7] McKay, C. and Fujinaga, I., Automatic Genre Classification Using Large High-Level Musical Feature Sets, 5th International Conference on Music Information Retrieval, 2004.
• [8] MusicXML 3.0 Specification | Recordare.com, 2011.