Abstract
At the dawn of a new era of intelligent applications in the music sector, the automatic genre-based classification of music tracks is a paramount task for the development of different services, such as music recommenders. In that sense, current solutions to uncover the genre of a song usually follow a multi-class approach revealing only a single genre per target song. However, songs do not usually belong to a single music genre but a mixture of them. In this context, the present work introduces SINATRA, a novel multi-label classifier of music genres of songs. By following an iterative procedure that continuously reduces the dimensional space of the genres, SINATRA is able to tag a song with multiple and complementary genres. The aforementioned dimensionality reduction is done by computing a graph comprising the co-occurrences of genres in songs. The evaluation results shows that SINATRA achieve an accuracy score above 0.5 given genre space covering more than 2,000 music genres.
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We assumed that the release date of a song was the one of the album on which it appeared.
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Acknowledgements
Financial support for this research has been provided under grant PID2020-112827GB-I00 funded by MCIN/AEI/10.13039/501100011033. It is also partially granted by the “EMERGIA” programme, funded by the Junta de Andalucía through the grant EMC21_004171.
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Terroso-Saenz, F., Soto, J., Muñoz, A. (2023). SINATRA: A Music Genre Classifier Based on Clustering and Graph Analysis. In: Daimi, K., Alsadoon, A., Coelho, L. (eds) Cutting Edge Applications of Computational Intelligence Tools and Techniques. Studies in Computational Intelligence, vol 1118. Springer, Cham. https://doi.org/10.1007/978-3-031-44127-1_9
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