Abstract
In this article the use of a matrix based representation of pieces is tested for the classification of musical pieces of some well known classical composers. The pieces in two corpora have been represented in two ways: matrices of interval pair probabilities and a set of 12 global features which had previously been used in a similar task. The classification accuracies of both representations have been computed using several supervised classification algorithms. A class binarization technique has also been applied to study how the accuracies change with this kind of methods. Promising results have been obtained which show that both the matrix representation and the class binarization techniques are suitable to be used in the automatic composer recognition problem.
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Acknowledgements
This work has been partially supported by the Basque Government Research Teams grant (IT900-16) and the Spanish Ministry of Economy and Competitiveness. TIN2015-64395-R (MINECO/FEDER).
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Goienetxea, I., Mendialdua, I., Sierra, B. (2018). On the Use of Matrix Based Representation to Deal with Automatic Composer Recognition. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_48
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DOI: https://doi.org/10.1007/978-3-030-03991-2_48
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