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Music artist style identification by semi-supervised learning from both lyrics and content
2004
Proceedings of the 12th annual ACM international conference on Multimedia - MULTIMEDIA '04
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. The approach for using a small set of labeled samples for the seed labeling to build classifiers that improve themselves using unlabeled data is presented. This approach is tested on a data set consisting of 43
doi:10.1145/1027527.1027612
dblp:conf/mm/LiO04
fatcat:gxwuj4mbqjcyde6ndt6r77w7gm