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dc.contributor.authorKolozali, Sefki
dc.date.accessioned2015-09-07T11:44:11Z
dc.date.available2015-09-07T11:44:11Z
dc.date.issued2014-03-17
dc.identifier.citationKolozali, S. 2014. Automatic Ontology Generation Based On Semantic Audio Analysis. Queen Mary University of Londonen_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/8452
dc.descriptionPhDen_US
dc.description.abstractOntologies provide an explicit conceptualisation of a domain and a uniform framework that represents domain knowledge in a machine interpretable format. The Semantic Web heavily relies on ontologies to provide well-defined meaning and support for automated services based on the description of semantics. However, considering the open, evolving and decentralised nature of the SemanticWeb – though many ontology engineering tools have been developed over the last decade – it can be a laborious and challenging task to deal with manual annotation, hierarchical structuring and organisation of data as well as maintenance of previously designed ontology structures. For these reasons, we investigate how to facilitate the process of ontology construction using semantic audio analysis. The work presented in this thesis contributes to solving the problems of knowledge acquisition and manual construction of ontologies. We develop a hybrid system that involves a formal method of automatic ontology generation for web-based audio signal processing applications. The proposed system uses timbre features extracted from audio recordings of various musical instruments. The proposed system is evaluated using a database of isolated notes and melodic phrases recorded in neutral conditions, and we make a detailed comparison between musical instrument recognition models to investigate their effects on the automatic ontology generation system. Finally, the automatically-generated musical instrument ontologies are evaluated in comparison with the terminology and hierarchical structure of the Hornbostel and Sachs organology system. We show that the proposed system is applicable in multi-disciplinary fields that deal with knowledge management and knowledge representation issues.en_US
dc.description.sponsorshipFundings from EPSRC, OMRAS-2 and NEMA projects.en_US
dc.language.isoenen_US
dc.publisherQueen Mary University of Londonen_US
dc.subjectCentre for Digital Musicen_US
dc.subjectElectronic Engineeringen_US
dc.subjectC4DMen_US
dc.titleAutomatic Ontology Generation Based On Semantic Audio Analysisen_US
dc.typeThesisen_US
dc.rights.holderThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author


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    Theses Awarded by Queen Mary University of London

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