Content management for electronic music distribution
Communications of the ACM - Digital rights management
Popular music access: the Sony music browser
Journal of the American Society for Information Science and Technology - Music information retrieval
A Semantic Web ontology for context-based classification and retrieval of music resources
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A music information system automatically generated via Web content mining techniques
Information Processing and Management: an International Journal
Exploring the music similarity space on the web
ACM Transactions on Information Systems (TOIS)
Hierarchical organization and description of music collections at the artist level
ECDL'05 Proceedings of the 9th European conference on Research and Advanced Technology for Digital Libraries
Case-based sequential ordering of songs for playlist recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Personalization in multimodal music retrieval
AMR'11 Proceedings of the 9th international conference on Adaptive Multimedia Retrieval: large-scale multimedia retrieval and evaluation
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Music classification is a key ingredient for electronicmusic distribution. Because of the lack of standards inmusic classification - or the lack of enforcement ofexisting standards - there is a huge amount ofunclassified titles of music in the world. In this paper wepropose a method of classification based on musical datamining technique based on co-occurrence and correlationanalysis that can be used for classification. It gives a newapproach of similarity between several titles of music orseveral artists. We study large corpora of textualinformation referring titles of music or artists whosenames are decided by humans without particularconstraints other than readability, and draw varioushypotheses concerning the natural similarities thatemerge from these corpora. Based on a clusteringtechnique, we show that interesting groups can revealspecific music genres and allow classifying titles of musicin a kind of objective manner.