Abstract
The article presents the possibility of classifying music tracks according to their musical genre. This issue is interesting because it is difficult to find solutions that look for similarity between songs based on their waveforms, as in this work. This article shows that such a classification is possible. For this process, the KNN classifier was used, for which it is possible to apply different metrics (metric spaces). The article shows the validity of testing different distance measures in the classification process. The analysis of music tracks and assignment to the appropriate genre is carried out, on the basis of attributes describing the music track. These attributes are obtained using the jAudio library. The development of further research in this area may allow finding other suitable music not only on the basis of historical data about the user (what he was listening to along with the music track) but also directly on the basis of the genre of the given song.
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Acknowledgements
This work was partly supported by BKM-509/RAU2/2017 and by Statutory Research funds of Institute of Informatics, Silesian University of Technology, Gliwice, Poland (grant No BK-213/RAU2/2018).
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Kostrzewa, D., Brzeski, R., Kubanski, M. (2018). The Classification of Music by the Genre Using the KNN Classifier. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety. BDAS 2018. Communications in Computer and Information Science, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-319-99987-6_18
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