An overview of audio information retrieval
Multimedia Systems - Special issue on audio and multimedia
Towards robust features for classifying audio in the CueVideo system
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
A robust audio classification and segmentation method
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Structured Coding for Content Based Interactive Audio
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Acoustic environment classification
ACM Transactions on Speech and Language Processing (TSLP)
Interactive multimedia system for distance learning of higher education
Edutainment'06 Proceedings of the First international conference on Technologies for E-Learning and Digital Entertainment
Human interaction categorization by using audio-visual cues
Machine Vision and Applications
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Semantic understanding of video is an important frontier in content based retrieval. In the research literature, significant attention has been given to the visual aspect of video, however, relatively little work directly uses audio content for video retrieval. Our paper gives an overview of our current research directions in semantic video retrieval using audio content. We discuss the effectiveness of classifying audio into semantic categories by combining both global and local audio features based in the frequency spectrum. Furthermore, we introduce two novel features called Frequency Spectrum Differentials (FSD), and Differential Swap Rate (DSR), that both model the shape of the spectrum.