Low-rank inverse-gamma @ IEEE ICASSP 2016 (Shangai)

Dionyssis Kounades-Bastian, Laurent Girin, Xavier Alameda-Pineda, Sharon Gannot and Radu Horaud

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Our low-rank parametrization of inverse-gamma distributions for speech modeling has been accepted at IEEE ICASSP 2016. The main idea is to parametrize the power-spectral density with an NMF-like model in which, even if the parameters are low-rank, the PSD is not. Indeed, the entries of the PSD matrix follow an inverse-gamma distribution, and the parameters of this distribution concatenated in a matrix are low rank. However, since the PSD itself is a hidden distribution, it is not forced to be low-rank. This is a bit difficult to explain in two lines. I am sure Dionyssos will do a great job explaining this at ICASSP. Check the paper [?].


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