Abstract
For music genre prediction typically low-level audio signal features from time, spectral or cepstral domains are taken into account. Another way is to use community-based statistics such as Last.FM tags. Whereas the first feature group often can not be clearly interpreted by listeners, the second one lacks in erroneous or not available data for less popular songs. We propose a two-level approach combining the specific advantages of the both groups: at first we create high-level descriptors which describe instrumental and harmonic characteristics of music content, some of them derived from low-level features by supervised classification or from analysis of extended chroma and chord features. The experiments show that each categorization task requires its own feature set.
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We thank the Klaus Tschira Foundation for the financial support.
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Vatolkin, I., Rötter, G., Weihs, C. (2014). Music Genre Prediction by Low-Level and High-Level Characteristics. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_46
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DOI: https://doi.org/10.1007/978-3-319-01595-8_46
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