As collections of digital music become larger and more widespread, there is a growing need for assistance in a user's navigation and interaction with a collection and with the individual members of that collection. Examining pairwise song relationships and similarities, based upon content derived features, provides a useful tool to do so. This paper looks into a means of extending a song classification algorithm to provide song to song similarity information. In order to evaluate the effectiveness of this method, the similarity data is used to group the songs into k-means clusters, these clusters are then compared against the original genre sorting algorithm.

Authors: Fields, Benjamin; Casey, Michael
Affiliation: Goldsmiths College, University of London
AES Convention: 123 (October 2007) Paper Number: 7267
Publication Date: October 1, 2007 Import into BibTeX
Subject: Applications in Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=14325

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