Detection of Acoustic Change-Points in Audio Streams and Signal Segmentation

Abstract

This contribution proposes an efficient method for the detection ofrelevant changes in continuous stream of sound. The detectedchange-points can then serve for the segmentation of long audiorecordings into shorter and more or less homogenous sections. First, wediscuss the task of a single change-point detection using the Bayesdecision theory. We show that it leads to a quite simple andcomputationally efficient solution based on the Bayesian InformationCriterion. Next, we extend this approach to formulate the algorithm forthe detection of multiple change-points. Finally, the proposedalgorithm is applied for the segmentation of broadcast newsaudio-streams into parts belonging to different speakers or differentacoustic conditions. Such segmentation is necessary as the first stepin the automatic speech-to-text transcription of TV or radio news.