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A Semantic Web ontology for context-based classification and retrieval of music resources

A Semantic Web ontology for context-based classification and retrieval of music resources. Alfio Ferrara, Luca A. Ludovico, Stefano Montanelli, Silvana Castano, and Goffredo Haus. ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 2, No. 3, Pages 177 – 198.

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A Semantic Web ontology for context-based classification and retrieval of music resources

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  1. A Semantic Web ontology for context-based classification and retrieval of music resources Alfio Ferrara, Luca A. Ludovico, Stefano Montanelli, Silvana Castano, and Goffredo Haus ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 2, No. 3, Pages 177 – 198

  2. I. Overview • A new semantic description of music resources based on context and genre • A new method of assigning music resources to genres based on context • An application to searching

  3. II. Semantic Description • The World Wide Web: human readable • The Semantic Web: machine readable • Ontologies

  4. III. The Ontology Genre Context

  5. IV. Context • Ensemble • Rhythm • Melody • Harmony

  6. IV. Context: Ensemble Ensemble has has Part Part has instrument has number of players Instrument #

  7. IV. Context: Rhythm Rhythm has has Episode Episode has time signature has number of measures # / # #

  8. IV. Context: Melody Melody has Fragment has scale with first degree has highest pitch has lowest pitch Note Note Note has has has Octave Pitch Accidental

  9. IV. Context: Harmony Harmony has Chord has has has Scale Fundamental Degree Bichord has degree distance has # Modifier

  10. V. Genre • Dimensions • ensemble (e.g., string quartet) • dance type (e.g., waltz) • critical (e.g., Romantic) • form (e.g., fugue)

  11. V. Genre • Fuzzy membership: “Along the genre dimension ‘Critical,’ resource R belongs to the category ‘Romantic’ with degree 0.7.” • Mapping from context to genre (incomplete): “If number_of_parts = 4 and for each part number_of_performers = 1, then along the genre dimension ‘Ensemble,’ R belongs to the category ‘Quartet’ with degree 1.0.”

  12. V. Genre • Other classification methods • manual • prescriptive (supervised) • emerging (unsupervised)

  13. VI. Recap: The Ontology Genre Mapping Context

  14. VII. Application to searching • Context-based discovery • Genre-based discovery • query by genre • query by target

  15. VII. Application to searching Query by genre Query by target

  16. VIII. Next steps • More classes and properties • Comparing different genre taxonomies • Extension to other media resources

  17. The MX Formalism • Layers • general • logical • structural • notational • performance • audio

  18. The MX Formalism B. Spine Space Time

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