Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29840
Title: A novel classification-based audio segmentation algorithm
Authors: Zhang, YB
Zhou, J
Bian, ZQ
Zhang, DP 
Keywords: Audio classification
Audio segmentation
False segmentation rate
Neural network
Issue Date: 2006
Publisher: 中國電子學會
Source: 電子學報 (Acta electronica sinica), 2006, v. 34, no. 4, p. 612-617 How to cite?
Journal: 電子學報 (Acta electronica sinica) 
Abstract: Content-based audio segmentation plays an important role in multimedia applications. Many conventional segmentation algorithms are based on small-scale classification and always result in a high false alarm rate. Our experimental results show that large-scale audio can be more easily classified than small ones, and this trend is irrespective of classifiers. According to this fact, we present a novel framework for audio segmentation to reduce the false segmentations. First, a rough segmentation step based on large-scale classification is taken to ensure the integrality of the content of segments. Then a subtle segmentation step based on small-scale classification is taken to further locate the segmentation points from the boundary areas computed by the rough segmentation step. Both theoretical analysis and experimental results show that nearly 3/4 false segmentation points can be reduced comparing to the conventional audio segmentation method based on small-scale audio classification, while preserving a low missing rate, when infrequently type-changed audio streams are dealt. So it can be concluded that it is very suitable for the real tasks such as music broadcast segmentation or music video analysis.
URI: http://hdl.handle.net/10397/29840
ISSN: 0372-2112
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