Testing a spectral-based feature set for audio genre classification

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dc.contributor.author Hartmann, Martin
dc.date.accessioned 2011-08-03T07:33:32Z
dc.date.available 2011-08-03T07:33:32Z
dc.date.issued 2011
dc.identifier.uri http://hdl.handle.net/123456789/36531
dc.identifier.uri http://urn.fi/URN:NBN:fi:jyu-2011080311207 en
dc.description.abstract Automatic musical genre classification is an important information retrieval task since it can be applied for practical purposes such as the organization of data collections in the digital music industry. However, this task remains an open question because the current state of the art shows far from satisfactory outcomes in terms of classification performance. Moreover, the most common algorithms that are used for this task are not designed for modelling music perception. This study suggests a framework for testing different musical features for use in music genre classification and evaluates the performance of this task based on two musical descriptors. The focus of this study is on automatic classification of music into genres based on audio content. The performance of two sets of timbral descriptors, namely the sub-band fluxes and the mel-frequency cepstral coefficients, is compared. The choice of these particular descriptors is based on their ease or difficulty of interpretation from a perceptual point of view. Classification performance is determined by using a variety of music datasets, learning algorithms, feature selection approaches and combinatorial feature subsets yielded from these descriptors. The results were estimated upon overall classification accuracies, generalization capability, and relevance of these musical descriptors based on feature ranking. According to the results, the sub-band fluxes, perceptually motivated descriptors of polyphonic timbre, performed better than the widely used mel-frequency cepstral coefficients. The former timbral descriptors showed better classification accuracies and lower tendency to overfit than the latter. In a nutshell, this study gives support to using perceptually interpretable timbre desciptors for musical genre classification tasks and suggests the utilization of the sub-band flux set for further content-based tasks in the field of music information retrieval.
dc.format.extent 79 s.
dc.language.iso eng
dc.rights This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. en
dc.rights Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. fi
dc.subject.other music information retrieval
dc.subject.other music genre classification
dc.subject.other polyphonic timbre
dc.subject.other feature ranking
dc.title Testing a spectral-based feature set for audio genre classification
dc.type Book en
dc.identifier.urn URN:NBN:fi:jyu-2011080311207
dc.subject.ysa musiikki
dc.subject.ysa genret
dc.subject.ysa sähköiset palvelut
dc.subject.ysa luokitus
dc.type.dcmitype Text en
dc.type.ontasot Pro gradu fi
dc.type.ontasot Master’s thesis en
dc.contributor.tiedekunta Humanistinen tiedekunta fi
dc.contributor.tiedekunta Faculty of Humanities en
dc.contributor.laitos Musiikin laitos fi
dc.contributor.laitos Department of Music en
dc.contributor.yliopisto University of Jyväskylä en
dc.contributor.yliopisto Jyväskylän yliopisto fi
dc.contributor.oppiaine Master's Degree Programme in Music, Mind and Technology en
dc.contributor.oppiaine Music, Mind and Technology (maisteriohjelma) fi
dc.subject.method mallintaminen
dc.date.updated 2011-08-03T07:33:32Z

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