Detection of Acoustic Change-Points in Audio Streams and Signal Segmentation
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This contribution proposes an efficient method for the detection of relevant changes in continuous stream of sound. The detected change-points can then serve for the segmentation of long audio recordings into shorter and more or less homogenous sections. First, we discuss the task of a single change-point detection using the Bayes decision theory. We show that it leads to a quite simple and computationally efficient solution based on the Bayesian Information Criterion. Next, we extend this approach to formulate the algorithm for the detection of multiple change-points. Finally, the proposed algorithm is applied for the segmentation of broadcast news audio-streams into parts belonging to different speakers or different acoustic conditions. Such segmentation is necessary as the first step in the automatic speech-to-text transcription of TV or radio news.
KeywordsAudio stream processing, detection of acoustic chan-ges, speaker segmentation, Bayesian information criterion, speech processing and recognition
Document typePeer reviewed
Document versionFinal PDF
SourceRadioengineering. 2005, vol. 14, č. 1, s. 37-40. ISSN 1210-2512
- 2005/1