Music Information Retrieval Using Social Tags and Audio

Music Information Retrieval Using Social Tags and Audio. Levy, M. & Sandler, M. Ieee Transactions on Multimedia, 11(3):383–395, 2009.
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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.

@article{levy_music_2009,
	title = {Music {Information} {Retrieval} {Using} {Social} {Tags} and {Audio}},
	volume = {11},
	issn = {1520-9210},
	doi = {10.1109/TMM.2009.2012913},
	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.},
	number = {3},
	journal = {Ieee Transactions on Multimedia},
	author = {Levy, M. and Sandler, M.},
	year = {2009},
	keywords = {\#nosource},
	pages = {383--395},
}

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