A novel classification-based audio segmentation algorithm

Yi Bin Zhang, Jie Zhou, Zhao Qi Bian, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


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.
Original languageChinese (Simplified)
Pages (from-to)612-617
Number of pages6
JournalTien Tzu Hsueh Pao/Acta Electronica Sinica
Issue number4
Publication statusPublished - 1 Apr 2006


  • Audio classification
  • Audio segmentation
  • False segmentation rate
  • Neural network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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