An overview of audio information retrieval
Multimedia Systems - Special issue on audio and multimedia
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
A Study on Content-Based Classification and Retrieval of Audio Database
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Emotional descriptors for map-based access to music libraries
ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
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In this paper, we propose an automatic classification system that classifies the Korean traditional music in digital library. In contrast to previous works, this paper focuses on the following issues of music classification. Firstly, the proposed system accepts query sound and automatically classifies input query into one of the six Korean traditional music categories such as “Court music”, “Classical chamber music”, “Folk song”, “Folk music”, “Buddhist music”, and “Shamanist music”. Secondly, in order to overcome system uncertainty due to the different query patterns, a robust feature extraction method called multi-feature clustering (MFC) combined with SFS feature selection is proposed. Finally, several pattern classification algorithms such as k-NN, Gaussian, GMM and SVM are tested and compared in terms of the classification accuracy. The experimental results indicate that the proposed MFC-SFS method shows more stable and higher classification performance than the one without the MFC-SFS.