An Industrial-strength content-based music recommendation system

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Title:	An Industrial-strength content-based music recommendation system
Author:	Cano, Pedro; Koppenberger, Markus; Wack, Nicolas
Abstract:	We present a metadata free system for the interaction with massive collections of music, the MusicSurfer. MusicSurfer automatically extracts descriptions related to instrumentation, rhythm and harmony from music audio signals. Together with efficient similarity metrics, the descriptions allow navigation of multimillion track music collections in a flexible and efficient way without the need of metadata or human ratings.
Abstract:	This work is partially funded by the European Union to the SIMAC IST-FP6-507142 project (http://www.semanticaudio.org).
Subject(s):	-Music content management -Music information retrieval -Content-based audio retrieval -Music recommendation
Rights:	© 2005 Association for Computing Machinery
Document type:	Conference Object Article - Accepted version
Published by:	ACM Association for Computer Machinery
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