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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 evaluationdoi:10.1145/1835449.1835555 dblp:conf/sigir/ShenMYPH10 fatcat:iysleyr5svcinjvdiuva2wconq