Abstract
This paper focuses on the relation between automatic tag prediction and music similarity. Intuitively music similarity measures based on auto-tags should profit from the improvement of the quality of the underlying audio tag predictors. We present classification experiments that verify this claim. Our results suggest a straight forward way to further improve content-based music similarity measures by improving the underlying auto-taggers.
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This research is supported by the Austrian Science Funds (FWF): P22856-N23 and Z159.
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Seyerlehner, K., Schedl, M., Sonnleitner, R., Hauger, D., Ionescu, B. (2014). From Improved Auto-Taggers to Improved Music Similarity Measures. In: Nürnberger, A., Stober, S., Larsen, B., Detyniecki, M. (eds) Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation. AMR 2012. Lecture Notes in Computer Science(), vol 8382. Springer, Cham. https://doi.org/10.1007/978-3-319-12093-5_11
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