@inproceedings{ ISI:000234179600058,
Author = {Bagci, U and Erzin, E},
Editor = {{Yolum, P and Gungor, T and Gurgen, F and Ozturan, C}},
Title = {{Boosting classifiers for music genre classification}},
Booktitle = {{COMPUTER AND INFORMATION SICENCES - ISCIS 2005, PROCEEDINGS}},
Series = {{LECTURE NOTES IN COMPUTER SCIENCE}},
Year = {{2005}},
Volume = {{3733}},
Pages = {{575-584}},
Note = {{20th International Symposium on Computer and Information Sciences,
   Istanbul, TURKEY, OCT 26-28, 2005}},
Organization = {{Sci \& Tech Res Council Turkey; Inst Elec \& Elect Engineers, Turkey
   Sect; Bogazici Univ Res Fund}},
Abstract = {{Music genre classification is an essential tool for music information
   retrieval systems and it has been finding critical applications in
   various media platforms. Two important problems of the automatic music
   genre classification are feature extraction and classifier design. This
   paper investigates discriminative boosting of classifiers to improve the
   automatic music genre classification performance. Two classifier
   structures, boosting of the Gaussian mixture model based classifiers and
   classifiers that are using the inter-genre similarity information, are
   proposed. The first classifier structure presents a novel extension to
   the maximum-likelihood based training of the Gaussian mixtures to
   integrate GMM classifier into boosting architecture. In the second
   classifier structure, the boosting idea is modified to better model the
   inter-genre similarity information over the mis-classified feature
   population. Once the inter-genre similarities are modeled, elimination
   of the inter-genre similarities reduces the inter-genre confusion and
   improves the identification rates. A hierarchical auto-clustering
   classifier scheme is integrated into the inter-genre similarity
   modeling. Experimental results with promising classification
   improvements are provided.}},
ISSN = {{0302-9743}},
ISBN = {{3-540-29414-7}},
ResearcherID-Numbers = {{Erzin, Engin/H-1716-2011
   Bagci, Ulas/A-4225-2012
   }},
ORCID-Numbers = {{Erzin, Engin/0000-0002-2715-2368
   Bagci, Ulas/0000-0001-7379-6829}},
Unique-ID = {{ISI:000234179600058}},
}

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Two important problems of the automatic music genre classification are feature extraction and classifier design. This paper investigates discriminative boosting of classifiers to improve the automatic music genre classification performance. Two classifier structures, boosting of the Gaussian mixture model based classifiers and classifiers that are using the inter-genre similarity information, are proposed. The first classifier structure presents a novel extension to the maximum-likelihood based training of the Gaussian mixtures to integrate GMM classifier into boosting architecture. In the second classifier structure, the boosting idea is modified to better model the inter-genre similarity information over the mis-classified feature population. Once the inter-genre similarities are modeled, elimination of the inter-genre similarities reduces the inter-genre confusion and improves the identification rates. 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Two important problems of the automatic music\n genre classification are feature extraction and classifier design. This\n paper investigates discriminative boosting of classifiers to improve the\n automatic music genre classification performance. Two classifier\n structures, boosting of the Gaussian mixture model based classifiers and\n classifiers that are using the inter-genre similarity information, are\n proposed. The first classifier structure presents a novel extension to\n the maximum-likelihood based training of the Gaussian mixtures to\n integrate GMM classifier into boosting architecture. In the second\n classifier structure, the boosting idea is modified to better model the\n inter-genre similarity information over the mis-classified feature\n population. Once the inter-genre similarities are modeled, elimination\n of the inter-genre similarities reduces the inter-genre confusion and\n improves the identification rates. 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