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SUMMARY:Itakura-Saito nonnegative factorizations of the power spectrogram 
 for music signal decomposition - Dr Cedric Fevotte\, CNRS - TELECOM ParisT
 ech
DTSTART:20100318T141500Z
DTEND:20100318T151500Z
UID:TALK22370@talks.cam.ac.uk
CONTACT:Rachel Fogg
DESCRIPTION:Nonnegative matrix factorization (NMF) is a popular linear reg
 ression technique in the fields of machine learning and signal/image proce
 ssing. \nMuch research about this topic has been driven by applications in
  audio. \nNMF has been for example applied with success to automatic music
  transcription and audio source separation\, where the data is usually tak
 en as the magnitude spectrogram of the sound signal\, and the Euclidean di
 stance or Kullback-Leibler divergence are used as measures of fit between 
 the original spectrogram and its approximate factorization.\n\nAfter a bri
 ef overview of NMF\, in this presentation we will show evidence of the rel
 evance of considering factorization of the power spectrogram\, with the It
 akura-Saito (IS) divergence. Indeed\, IS-NMF is shown to be connected to m
 aximum likelihood inference of variance parameters in a well-defined stati
 stical model of superimposed Gaussian components and this model is in turn
  shown to be well suited to audio. \nFurthermore\, the statistical setting
  opens doors to Bayesian approaches and to a variety of computational infe
 rence techniques. We discuss in particular model order selection strategie
 s and Markov regularization of the activation matrix\, to account for time
 -persistence in audio.\n\nThis presentation will also adress extensions of
  NMF to the multichannel case\, in both instantaneous or convolutive recor
 dings\, possibly underdetermined\, leading to nonnegative tensor factoriza
 tions under novel structures. We will present in particular audio source s
 eparation results of real-world stereo musical excerpts.\n\nReferences :\n
 \nC. Févotte\, N. Bertin and J.-L. Durrieu. "Nonnegative matrix factoriza
 tion with the Itakura-Saito divergence. With application to music analysis
 \," Neural Computation\, vol. 21\, no 3\, Mar. 2009 http://www.tsi.enst.fr
 /~fevotte/Journals/neco09_is-nmf.pdf\n\nA. Ozerov and C. Févotte. "Multic
 hannel nonnegative matrix factorization in convolutive mixtures for audio 
 source separation\," IEEE Trans. Audio\, Speech and Language Processing\, 
 2010 (to appear) http://www.tsi.enst.fr/~fevotte/TechRep/techrep09_multinm
 f.pdf\n
LOCATION:LR5\, Engineering\, Department of
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