Abstract
In this paper we are interested in musical data classification. For musical features representation, we propose to adopt a histogram structure in order to preserve a maximum amount of information. The melodic dimension of the data is described in terms of pitch values, pitch intervals, melodic direction and durations of notes as well as silences. Our purpose is to have a data representation well suited to a generic framework for classifying melodies by means of known supervised Machine Learning (ML) algorithms. Since such algorithms are not expected to handle histogram-based feature values, we propose to transform the representation space in the pattern recognition process. This transformation is realized by partitioning the domain of each attribute using a clustering technique. The model is evaluated experimentally by implementing three kinds of classifiers (musical genre, composition style and emotional content).
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Naccache, M., Borgi, A., Ghédira, K. (2009). A Learning-Based Model for Musical Data Representation Using Histograms. In: Ystad, S., Kronland-Martinet, R., Jensen, K. (eds) Computer Music Modeling and Retrieval. Genesis of Meaning in Sound and Music. CMMR 2008. Lecture Notes in Computer Science, vol 5493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02518-1_14
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DOI: https://doi.org/10.1007/978-3-642-02518-1_14
Publisher Name: Springer, Berlin, Heidelberg
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