Abstract
In this talk I will relate current work at Google in music recommendation to the challenge of automatic music annotation (“autotagging”). I will spend most of the talk looking at (a) signal processing and sparse coding strategies for pulling relevant structure from audio, and (b) training multi-class ranking models in order to build good music similarity spaces. Although I will describe some technical aspects of autotagging and ranking via embedding, the main goal of the talk is to foster a better understanding of the real-world challenges we face in helping users find music they’ll love. To this end I will play a number of audio demos illustrating what we can (and cannot) hope to achieve by working with audio.
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© 2012 Springer-Verlag Berlin Heidelberg
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Eck, D. (2012). Machine Learning Methods for Music Discovery and Recommendation. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33460-3_3
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DOI: https://doi.org/10.1007/978-3-642-33460-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33459-7
Online ISBN: 978-3-642-33460-3
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