Music information retrieval using social tags and audio

  • Authors:

  • Mark Levy;Mark Sandler

  • Affiliations:

  • Last.fm, London, UK and Centre for Digital Music, Department of Electronic Engineering, Queen Mary, University of London, London, UK;Centre for Digital Music, Department of Electronic Engineering, Queen Mary, University of London, London, UK

  • Venue:
  • IEEE Transactions on Multimedia - Special section on communities and media computing
  • Year:
  • 2009

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Abstract

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.