Latent Variable Analysis and Signal Separation: 9th International Conference, LVA/ICA 2010, St. Malo, France, September 27-30, 2010, Proceedings
Vincent Vigneron, Vicente Zarzoso, Eric Moreau, Rémi Gribonval, Emmanuel Vincent
Springer Science & Business Media, Sep 27, 2010 - Computers - 655 pages
Thisvolumecollectsthepaperspresentedatthe9thInternationalConferenceon Latent Variable Analysis and Signal Separation,LVA/ICA 2010. The conference was organized by INRIA, the French National Institute for Computer Science and Control,and was held in Saint-Malo, France, September 27–30,2010,at the Palais du Grand Large. Tenyearsafterthe?rstworkshoponIndependent Component Analysis(ICA) in Aussois, France, the series of ICA conferences has shown the liveliness of the community of theoreticians and practitioners working in this ?eld. While ICA and blind signal separation have become mainstream topics, new approaches have emerged to solve problems involving signal mixtures or various other types of latent variables: semi-blind models, matrix factorization using sparse com- nent analysis, non-negative matrix factorization, probabilistic latent semantic indexing, tensor decompositions, independent vector analysis, independent s- space analysis, and so on. To re?ect this evolution towards more general latent variable analysis problems in signal processing, the ICA International Steering Committee decided to rename the 9th instance of the conference LVA/ICA. From more than a hundred submitted papers, 25 were accepted as oral p- sentationsand53 asposter presentations. Thecontent ofthis volumefollowsthe conference schedule, resulting in 14 chapters. The papers collected in this v- ume demonstrate that the research activity in the ?eld continues to range from abstract concepts to the most concrete and applicable questions and consid- ations. Speech and audio, as well as biomedical applications, continue to carry the mass of the applications considered.
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applied approach assume audio source separation beamforming Berlin Heidelberg 2010 blind separation blind signal separation blind source separation channel clustering coeﬃcients computed correlation cost function covariance matrix dataset decomposition deﬁned deﬁnition demixing denotes dictionary learning diﬀerent distribution eﬀect eﬃcient estimation evaluation extraction FastICA ﬁeld ﬁlter ﬁnd ﬁnding ﬁrst ﬁxed frequency domain Gaussian IEEE IEEE Trans Independent Component Analysis iteration joint diagonalization kurtosis linear LNCS LVA/ICA microphone minimization mixing matrix Neural noise non-negative matrix factorization nonlinear nonnegative matrix number of sources observed obtained optimization orthogonal outliers output parameters performance permutation problem Proc proposed method random robust samples Signal Processing simulations solution source signals sparse coding sparse representation sparsity spatial spectral spectrogram speech signals Springer-Verlag Berlin Heidelberg statistics STFT structure subband subspace techniques tensor time-frequency tion update values variables vector Vigneron