Audio Segmentation via Tri-Model Bayesian Information Criterion

Yunfeng Du, Wei Hu, Yonghong Yan, Tao Wang, Yimin Zhang

Abstract: This paper addresses the problem of audio segmentation in practical media (e.g. TV series, movies and etc.) which usually consists of segments in various lengths with quite a portion of short ones. An unsupervised audio segmentation approach is presented, including a segmentation-stage to detect potential acoustic changes, and a refinement-stage to refine these candidate changes by a tri-model Bayesian information criterion. Experiments show that the proposed approach has good detectability of short segments and the novel tri-model BIC effectively improves the overall segmentation performance.