Machine Learning Methods for Music Discovery and Recommendation
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.
Cite
Text
Eck. "Machine Learning Methods for Music Discovery and Recommendation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33460-3_3Markdown
[Eck. "Machine Learning Methods for Music Discovery and Recommendation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/eck2012ecmlpkdd-machine/) doi:10.1007/978-3-642-33460-3_3BibTeX
@inproceedings{eck2012ecmlpkdd-machine,
title = {{Machine Learning Methods for Music Discovery and Recommendation}},
author = {Eck, Douglas},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {4},
doi = {10.1007/978-3-642-33460-3_3},
url = {https://mlanthology.org/ecmlpkdd/2012/eck2012ecmlpkdd-machine/}
}