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Music Information Retrieval Using Social Tags and Audio

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2 Author(s)
Mark Levy ; Last.fm, London ; Mark Sandler

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.

Published in:

IEEE Transactions on Multimedia  (Volume:11 ,  Issue: 3 )