https://hal.inria.fr/hal-01580733Liutkus, AntoineAntoineLiutkusMULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication - Inria Nancy - Grand Est - Inria - Institut National de Recherche en Informatique et en Automatique - LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery - LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications - Inria - Institut National de Recherche en Informatique et en Automatique - UL - Université de Lorraine - CNRS - Centre National de la Recherche ScientifiqueYoshii, KazuyoshiKazuyoshiYoshiiKyoto UniversityA diagonal plus low-rank covariance model for computationally efficient source separationHAL CCSD2017[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingLiutkus, Antoine - Démixage en ligne de larges archives sonores - - KAMoulox2015 - ANR-15-CE38-0003 - AAPG2015 - VALID - 2017-09-01 21:08:572023-03-24 14:53:052017-09-04 10:07:16enConference papersapplication/pdf1This paper presents an accelerated version of positive semidef-inite tensor factorization (PSDTF) for blind source separation. PSDTF works better than nonnegative matrix factoriza-tion (NMF) by dropping the arguable assumption that audio signals can be whitened in the frequency domain by using short-term Fourier transform (STFT). Indeed, this assumption only holds true in an ideal situation where each frame is infinitely long and the target signal is completely stationary in each frame. PSDTF thus deals with full covariance matrices over frequency bins instead of forcing them to be diagonal as in NMF. Although PSDTF significantly outperforms NMF in terms of separation performance, it suffers from a heavy computational cost due to the repeated inversion of big covariance matrices. To solve this problem, we propose an intermediate model based on diagonal plus low-rank covariance matrices and derive the expectation-maximization (EM) algorithm for efficiently updating the parameters of PSDTF. Experimental results showed that our method can dramatically reduce the complexity of PSDTF by several orders of magnitude without a significant decrease in separation performance. Index Terms— Blind source separation, nonnegative matrix factorization, positive semidefinite tensor factorization, low-rank approximation.