combining audio content and social context for semantic music discovery
When attempting to annotate music, it is important to consider both acoustic content and social context. This paper explores techniques for collecting and combining multiple sources of such information for the purpose of building a query-by-text music retrieval system. We consider two representations of the acoustic content (related to timbre and harmony) and two social sources (social tags and web documents). We then compare three algorithms that combine these information sources: calibrated score averaging (CSA), RankBoost, and kernel combination support vector machines (KC-SVM). We demonstrate empirically that each of these algorithms is superior to algorithms that use individual information sources.
calibrated score averaging rankboost music ir kernel combination svm combining data sources