Hari Sundaram, Shih-Fu Chang. Audio Scene Segmentation using Multiple Models, Features and Time Scales. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Istanbul, Turkey, June 2000.
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In this paper 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) A definition of an audio scene (b) multiple feature models that characterize the dominant sources and (c) 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%
@InProceedings{dvmmPub93,
Author = {Sundaram, Hari and Chang, Shih-Fu},
Title = {Audio Scene Segmentation using Multiple Models, Features and Time Scales},
BookTitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
Address = {Istanbul, Turkey},
Month = {June},
Year = {2000}
}
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