Abstract
The number of music in digital format increases years after years. The amount of data available allows streaming services to offer wide variety of music. This makes these services attractive. Automatic classification process is required to manage and structure all files available. Automatic music classification is an active field of research. Researches rely on machine learning techniques such as deep neural network. These techniques used features extracted from raw data to generate classification. Features have an important impact on results. Selecting the right descriptors is one of the main difficulties associated with automatic music classification. Opacity of current methods makes it difficult to evaluate the contribution of descriptors in the classification process. In this paper, we propose to use association rules to add more transparency and interpretability.
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Rompré, L., Biskri, I., Meunier, JG. (2019). Identifying Similarities Between Musical Files Using Association Rules. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_38
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