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.
Original language | Chinese (Simplified) |
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Pages (from-to) | 612-617 |
Number of pages | 6 |
Journal | Tien Tzu Hsueh Pao/Acta Electronica Sinica |
Volume | 34 |
Issue number | 4 |
Publication status | Published - 1 Apr 2006 |
Keywords
- Audio classification
- Audio segmentation
- False segmentation rate
- Neural network
ASJC Scopus subject areas
- Electrical and Electronic Engineering