Audio Segmentation via Tri-Model Bayesian Information Criterion

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.