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Proceedings Paper

Media segmentation using self-similarity decomposition
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Paper Abstract

We present a framework for analyzing the structure of digital media streams. Though our methods work for video, text, and audio, we concentrate on detecting the structure of digital music files. In the first step, spectral data is used to construct a similarity matrix calculated from inter-frame spectral similarity.The digital audio can be robustly segmented by correlating a kernel along the diagonal of the similarity matrix. Once segmented, spectral statistics of each segment are computed. In the second step,segments are clustered based on the self-similarity of their statistics. This reveals the structure of the digital music in a set of segment boundaries and labels. Finally, the music is summarized by selecting clusters with repeated segments throughout the piece. The summaries can be customized for various applications based on the structure of the original music.

Paper Details

Date Published: 10 January 2003
PDF: 9 pages
Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); doi: 10.1117/12.476302
Show Author Affiliations
Jonathan T. Foote, FX Palo Alto Lab., Inc. (United States)
Matthew L. Cooper, FX Palo Alto Lab., Inc. (United States)

Published in SPIE Proceedings Vol. 5021:
Storage and Retrieval for Media Databases 2003
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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