Music Information Retrieval Using Social Tags and Audio

In this paper we describe a novel approach to applying text-based information retrieval techniques to music collections. We represent tracks with a joint vocabulary consisting of both conventional words, drawn from social tags, and audio muswords , representing characteristics of automatically-ident...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2009-04, Vol.11 (3), p.383-395
Main Authors: Levy, M., Sandler, M.
Format: Article
Language:English
Subjects:
Audio
Collaborative work
Collection
Construction
Filtering
Fingerprint recognition
Indexing
Information retrieval
Mood
Multimedia
Music
Music information retrieval
Navigation
Recommender systems
Retrieval
social tags
Tags
Vocabulary
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Description
Summary:In this paper we describe a novel approach to applying text-based information retrieval techniques to music collections. We represent tracks with a joint vocabulary consisting of both conventional words, drawn from social tags, and audio muswords , representing characteristics of automatically-identified regions of interest within the signal. We build vector space and latent aspect models indexing words and muswords for a collection of tracks, and show experimentally that retrieval with these models is extremely well-behaved. We find in particular that retrieval performance remains good for tracks by artists unseen by our models in training, and even if tags for their tracks are extremely sparse.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2009.2012913