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Audio scene segmentation using multiple features, models and time scales

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2 Author(s)
H. Sundaram ; Dept. of Electr. Eng., Columbia Univ., New York, NY, USA ; S. -F. Chang

We present an algorithm for audio scene segmentation. An audio scene is a semantically consistent sound segment that is characterized by a few dominant sources of sound. A scene change occurs when a majority of the sources present in the data change. Our segmentation framework has three parts: a definition of an audio scene; multiple feature models that characterize the dominant sources; and a simple, causal listener model, which mimics human audition using multiple time-scales. We define a correlation function that determines correlation with past data to determine segmentation boundaries. The algorithm was tested on a difficult data set, a 1 hour audio segment of a film, with impressive results. It achieves an audio scene change detection accuracy of 97%

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Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:6 )

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