Music tagging with regularized logistic regression

Xie, B; Bian, W; Tao, D; Chordia, P

Music tagging with regularized logistic regression

Xie, B Bian, W Tao, D

Chordia, P

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011, 2011, pp. 711 - 716
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Xie, B	en_US
dc.contributor.author	Bian, W	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Chordia, P	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011, 2011, pp. 711 - 716	en_US
dc.identifier.isbn	9780615548654	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120470
dc.description.abstract	In this paper, we present a set of simple and efficient regularized logistic regression algorithms to predict tags of music. We first vector-quantize the delta MFCC features using k-means and construct "bag-of-words" representation for each song. We then learn the parameters of these logistic regression algorithms from the "bag-of- words" vectors and ground truth labels in the training set. At test time, the prediction confidence by the linear classifiers can be used to rank the songs for music annotation and retrieval tasks. Thanks to the convex property of the objective functions, we adopt an efficient and scalable generalized gradient method to learn the parameters, with global optimum guaranteed. And we show that these efficient algorithms achieve stateof- the-art performance in annotation and retrieval tasks evaluated on CAL-500. © 2011 International Society for Music Information Retrieval.	en_US
dc.relation.ispartof	Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011	en_US
dc.title	Music tagging with regularized logistic regression	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

In this paper, we present a set of simple and efficient regularized logistic regression algorithms to predict tags of music. We first vector-quantize the delta MFCC features using k-means and construct "bag-of-words" representation for each song. We then learn the parameters of these logistic regression algorithms from the "bag-of- words" vectors and ground truth labels in the training set. At test time, the prediction confidence by the linear classifiers can be used to rank the songs for music annotation and retrieval tasks. Thanks to the convex property of the objective functions, we adopt an efficient and scalable generalized gradient method to learn the parameters, with global optimum guaranteed. And we show that these efficient algorithms achieve stateof- the-art performance in annotation and retrieval tasks evaluated on CAL-500. © 2011 International Society for Music Information Retrieval.

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http://hdl.handle.net/10453/120470