A Novel Classification-Based Audio Segmentation Algorithm

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.