Abstract
The current research efforts in the field of video parsing and analysis are mainly focused on the use of pictorial information, while neglecting an important supplementary source of content information such as the embedded audio or soundtrack. In contrast, in this paper we address the issue of exploiting audio information that can be jointly used with video information for scene changes detection. The proposed method directly works on MPEG encoded sequences so to avoid computationally intensive decoding procedures. It is based on a multi-expert classification system made up of a hierarchical ensemble of neural networks.
Finally, after presentation of a large audio database, suitably designed for assessing the performance of the approach, preliminary experimental results are discussed.
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De Santo, M., Percannella, G., Sansone, C., Vento, M. (2001). A Neural Multi -Expert Classification System for MPEG Audio Segmentation. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_6
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DOI: https://doi.org/10.1007/3-540-44732-6_6
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