IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Audio as a Support to Scene Change Detection and Characterization of Video Sequences
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Real-time discrimination of broadcast speech/music
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
A fast audio classification from MPEG coded data
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Extracting story units from long programs for video browsing and navigation
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Improving Acoustic Models with Captioned Multimedia Speech
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
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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 multiexpert 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.