@article{ ISI:000248234800004,
Author = {Bagci, Ulas and Erzin, Engin},
Title = {{Automatic classification,of musical genres using inter-genre similarity}},
Journal = {{IEEE SIGNAL PROCESSING LETTERS}},
Year = {{2007}},
Volume = {{14}},
Number = {{8}},
Pages = {{521-524}},
Month = {{AUG}},
Abstract = {{Musical genre classification is an essential tool for music information
   retrieval systems and it has potential to become a highly demanded
   application in various media platforms. Two important problems of the
   automatic musical genre classification are feature extraction and
   classifier design. In this letter, we propose two novel classifiers
   using inter-genre similarity (IGS) modeling and investigate the use of
   dynamic timbral texture features in order to improve automatic musical
   genre classification performance. Inter-genre similarity is modeled over
   hard-to-classify samples of the musical genre feature space. In the
   classification, samples within inter-genre similarity class are
   eliminated to reduce inter-genre confusion and to improve genre
   classification performance. Experimental results show that the proposed
   classifiers provide better classification rates than the existing
   methods.}},
DOI = {{10.1109/LSP.2006.891320}},
ISSN = {{1070-9908}},
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:000248234800004}},
}

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