Towards a new reading experience via semantic fusion of text and music
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Supervised dictionary learning for music genre classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Mood classification of Indian popular music
Proceedings of the CUBE International Information Technology Conference
Genre classification of symbolic music with SMBGT
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Unsupervised tagging of spanish lyrics dataset using clustering
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Optimizing cepstral features for audio classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Audio classification with low-rank matrix representation features
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Initial objective & subjective evaluation of a similarity-based audio compression technique
Proceedings of the 8th Audio Mostly Conference
Learning to Recommend Descriptive Tags for Questions in Social Forums
ACM Transactions on Information Systems (TOIS)
Classification accuracy is not enough
Journal of Intelligent Information Systems
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Music information retrieval (MIR) is an emerging research area that receives growing attention from both the research community and music industry. It addresses the problem of querying and retrieving certain types of music from large music data set. Classification is a fundamental problem in MIR. Many tasks in MIR can be naturally cast in a classification setting, such as genre classification, mood classification, artist recognition, instrument recognition, etc. Music annotation, a new research area in MIR that has attracted much attention in recent years, is also a classification problem in the general sense. Due to the importance of music classification in MIR research, rapid development of new methods, and lack of review papers on recent progress of the field, we provide a comprehensive review on audio-based classification in this paper and systematically summarize the state-of-the-art techniques for music classification. Specifically, we have stressed the difference in the features and the types of classifiers used for different classification tasks. This survey emphasizes on recent development of the techniques and discusses several open issues for future research.