A music search engine built upon audio-based and web-based similarity measures
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A distributed data space for music-related information
Workshop on multimedia information retrieval on The many faces of multimedia semantics
Towards a Media Interpretation Framework for the Semantic Web
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Query Answering for OWL-DL with rules
Web Semantics: Science, Services and Agents on the World Wide Web
Audio feature engineering for automatic music genre classification
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
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Although technologies of both low-level and high-level descriptors for music information retrieval (MIR) are advancing, there are some essential deficiencies while utilizing them separately. In this paper we propose a model where the low-level and high-level descriptors collaborate to support semantics-based MIR. The ontology of “Music Scene” domain is constructed as a demonstration, and a set of domain related low-level and high-level descriptor analyses are introduced. Given the domain ontology and the analysis results as input, an abduction process is adopted to compute the semantics-based interpretations. Evaluations show that the collaborative model does not only give a better recall rate of semantics-based retrieval than separated models, but also maintains a promising precision meanwhile.