05977245

a

05977245 Su, Ja-Hwung Hung, Tzu-Shiang Lee, Chun-Jen Lu, Chung-Li Chang, Wei-Lun Tseng, Vincent S. Effective content-based music retrieval with pattern-based relevance feedback. K\"onig, Andreas (ed.) et al., Knowlege-based and intelligent information and engineering systems. 15th international conference, KES 2011, Kaiserslautern, Germany, September 12--14, 2011. Proceedings, Part II. Berlin: Springer (ISBN 978-3-642-23862-8/pbk). Lecture Notes in Computer Science 6882. Lecture Notes in Artificial Intelligence, 285-295 (2011). 2011 Berlin: Springer EN content-based music retrieval pattern-based relevance feedback query point movement query re-weighting query expansion

doi:10.1007/978-3-642-23863-5_29

Summary: To retrieve the preferred music piece from a music database, contentbased music retrieval has been studied for several years. However, it is not easy to retrieve the desired music pieces within only one query process. It motivates us to propose a novel query refinement technique called PBRF (Pattern-based Relevance Feedback) to predict the user's preference on music via a series of feedbacks, which combines three kinds of query refinement strategies, namely QPM (Query Point Movement), QR (Query Reweighting) and QEX (Query Expansion). In this work, each music piece is transformed into a pattern string, and the related discriminability and representability of each pattern can be calculated then. According to the information of discriminability and representability calculated, the user's preference on music can be retrieved by matching patterns of music pieces in the music database with those of a query music piece. In addition, with considering the local-optimal problem, extensive and intensive search methods based on user's feedbacks are proposed to approximate the successful search. Through the integration of QPM, QR, QEX and switch-based search strategies, the user's intention can be captured more effectively. The experimental results reveal that our proposed approach performs better than existing methods in terms of effectiveness.