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Alexey Ozerov
Researcher at French Institute for Research in Computer Science and Automation
Publications - 83
Citations - 3546
Alexey Ozerov is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Source separation & Blind signal separation. The author has an hindex of 26, co-authored 82 publications receiving 3175 citations. Previous affiliations of Alexey Ozerov include InterDigital, Inc. & Télécom ParisTech.
Papers
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Journal ArticleDOI
Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation
Alexey Ozerov,Cédric Févotte +1 more
TL;DR: In this article, a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined convolutive mixture of source signals, is considered.
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A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation
TL;DR: This paper proposes to analyze a large number of established and recent techniques according to four transverse axes: 1) the acoustic impulse response model, 2) the spatial filter design criterion, 3) the parameter estimation algorithm, and 4) optional postfiltering.
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A General Flexible Framework for the Handling of Prior Information in Audio Source Separation
TL;DR: This paper introduces a general audio source separation framework based on a library of structured source models that enable the incorporation of prior knowledge about each source via user-specifiable constraints.
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Multi-source TDOA estimation in reverberant audio using angular spectra and clustering
TL;DR: Five new TDOA estimation methods inspired from signal-to-noise-ratio (SNR) weighting and probabilistic multi-source modeling techniques that have been successful for anechoicTDOA estimation and audio source separation are introduced and evaluated.
Journal ArticleDOI
Adaptation of Bayesian Models for Single-Channel Source Separation and its Application to Voice/Music Separation in Popular Songs
TL;DR: A general formalism for source model adaptation which is expressed in the framework of Bayesian models is introduced and results show that an adaptation scheme can improve consistently and significantly the separation performance in comparison with nonadapted models.