Conference Proceeding Article
Music information retrieval (MIR) holds great promise as a technology for managing large music archives. One of the key components of MIR that has been actively researched into is music tagging. While significant progress has been achieved, most of the existing systems still adopt a simple classification approach, and apply machine learning classifiers directly on low level acoustic features. Consequently, they suffer the shortcomings of (1) poor accuracy, (2) lack of comprehensive evaluation results and the associated analysis based on large scale datasets, and (3) incomplete content representation, arising from the lack of multimodal and temporal information integration. In this paper, we introduce a novel system called MMTagger that effectively integrates both multimodal and temporal information in the representation of music signal. The carefully designed multilayer architecture of the proposed classification framework seamlessly combines Multiple Gaussian Mixture Models (GMMs) and Support Vector Machine (SVM) into a single framework. The structure preserves more discriminative information, leading to more accurate and robust tagging. Experiment results obtained with two large music collections highlight the various advantages of our multilayer framework over state of the art techniques.
Browsing, Music information retrieval, Search, Tagging
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
SIGIR 2010: Proceedings: 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval: Geneva, Switzerland, July 19-23, 2010
City or Country
SHEN, Jialie; WANG, Meng; YAN, Shuicheng; PANG, Hwee Hwa; and HUA, Xian-Sheng.
Effective Music Tagging through Advanced Statistical Modeling. (2010). SIGIR 2010: Proceedings: 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval: Geneva, Switzerland, July 19-23, 2010. 635-642. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/515
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