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An information-geometric approach to real-time audio segmentation

Arnaud Dessein 1, 2 Arshia Cont 1, 3 
1 MuTant - Synchronous Realtime Processing and Programming of Music Signals
IRCAM - Institut de Recherche et Coordination Acoustique/Musique, Inria Paris-Rocquencourt, UPMC - Université Pierre et Marie Curie - Paris 6, CNRS - Centre National de la Recherche Scientifique
3 Musical Representations
STMS - Sciences et Technologies de la Musique et du Son
Abstract : We present a generic approach to real-time audio segmentation in the framework of information geometry for exponential families. The proposed system detects changes by monitoring the information rate of the signals as they arrive in time. We also address shortcomings of traditional cumulative sum approaches to change detection, which assume known parameters before change. This is done by considering exact generalized likelihood ratio test statistics, with a complete estimation of the unknown parameters in the respective hypotheses. We derive an efficient sequential scheme to compute these statistics through convex duality. We finally provide results for speech segmentation in speakers, and polyphonic music segmentation in note slices.
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Submitted on : Sunday, February 24, 2013 - 4:56:08 PM
Last modification on : Tuesday, March 15, 2022 - 3:22:17 AM
Long-term archiving on: : Saturday, May 25, 2013 - 4:06:48 AM


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Arnaud Dessein, Arshia Cont. An information-geometric approach to real-time audio segmentation. IEEE Signal Processing Letters, 2013, 20 (4), pp.331-334. ⟨10.1109/LSP.2013.2247039⟩. ⟨hal-00793999⟩



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