Su, Min YianMin YianSuYI-HSUAN YANGLin, Yu ChingYu ChingLinHOMER H. CHEN2023-10-242023-10-242009-12-019780981353708https://scholars.lib.ntu.edu.tw/handle/123456789/636483Music boundary detection is a fundamental step of music analysis and summarization. Existing works use either unsupervised or supervised methodologies to detect boundary. In this paper, we propose an integrated approach that takes advantage of both methodologies. In particular, a graph-theoretic approach is proposed to fuse the results of an unsupervised model and a supervised one by the knowledge of the typical length of a music section. To further improve accuracy, a number of novel mid-level features are developed and incorporated to the boundary detection framework. Evaluation result on the RWC dataset shows the effectiveness of the proposed approach. © 2009 International Society for Music Information Retrieval.conference paper2-s2.0-84856929813https://api.elsevier.com/content/abstract/scopus_id/84856929813