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Cédric Févotte - One of the best experts on this subject based on the ideXlab platform.

  • Multichannel nonnegative tensor factorization with structured constraints for user-guided audio source separation
    2011
    Co-Authors: Alexey Ozerov, Cédric Févotte, Raphaël Blouet, Jean-louis Durrieu
    Abstract:

    Separating multiple tracks from professionally produced music recordings (PPMRs) is still a challenging problem. We address this task with a user-guided approach in which the separation system is provided segmental information indicating the time activations of the particular instruments to separate. This information may typically be retrieved from manual annotation. We use a so-called multichannel nonnegative tensor factorization (NTF) model, in which the original sources are observed through a multichannel Convolutive Mixture and in which the source power spectrograms are jointly modeled by a 3-valence (time/frequency/source) tensor. Our user-guided separation method produced competitive results at the 2010 Signal Separation Evaluation Campaign, with sufficient quality for real-world music editing applications.

  • Multichannel nonnegative tensor factorization with structured constraints for userguided audio source separation
    2011
    Co-Authors: Alexey Ozerov, Cédric Févotte, Raphaël Blouet, Jean-louis Durrieu
    Abstract:

    Separating multiple tracks from professionally produced music recordings (PPMRs) is still a challenging problem. We address this task with a user-guided approach in which the separation system is provided segmental information indicating the time activations of the particular instruments to separate. This information may typically be retrieved from manual annotation. We use a so-called multichannel nonnegative tensor factorization (NTF) model, in which the original sources are observed through a multichannel Convolutive Mixture and in which the source power spectrograms are jointly modeled by a 3-valence (time/frequency/source) tensor. Our user-guided separation method produced competitive results at the 2010 Signal Separation Evaluation Campaign, with sufficient quality for real-world music editing applications. Index Terms — Audio source separation, user-guided, nonnegative tensor factorization, generalized expectation maximization

  • multichannel nonnegative matrix factorization in Convolutive Mixtures for audio source separation
    IEEE Transactions on Audio Speech and Language Processing, 2010
    Co-Authors: Alexey Ozerov, Cédric Févotte
    Abstract:

    We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined Convolutive Mixture of source signals. We work in the short-time Fourier transform (STFT) domain, where convolution is routinely approximated as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization (EM) algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms are applied to stereo audio source separation in various settings, covering blind and supervised separation, music and speech sources, synthetic instantaneous and Convolutive Mixtures, as well as professionally produced music recordings. Our EM method produces competitive results with respect to state-of-the-art as illustrated on two tasks from the international Signal Separation Evaluation Campaign (SiSEC 2008).

  • C.: Multichannel nonnegative matrix factorization in Convolutive Mixtures for audio source separation
    2010
    Co-Authors: Alexey Ozerov, Cédric Févotte
    Abstract:

    We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined Convolutive Mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance. Index Terms — Multichannel audio, nonnegative matrix factorization, nonnegative tensor factorization, underdetermined Convolutive blind source separation. 1

  • multichannel nonnegative matrix factorization in Convolutive Mixtures with application to blind audio source separation
    International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Alexey Ozerov, Cédric Févotte
    Abstract:

    We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly under-determined Convolutive Mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance.

Alexey Ozerov - One of the best experts on this subject based on the ideXlab platform.

  • Multichannel nonnegative tensor factorization with structured constraints for user-guided audio source separation
    2011
    Co-Authors: Alexey Ozerov, Cédric Févotte, Raphaël Blouet, Jean-louis Durrieu
    Abstract:

    Separating multiple tracks from professionally produced music recordings (PPMRs) is still a challenging problem. We address this task with a user-guided approach in which the separation system is provided segmental information indicating the time activations of the particular instruments to separate. This information may typically be retrieved from manual annotation. We use a so-called multichannel nonnegative tensor factorization (NTF) model, in which the original sources are observed through a multichannel Convolutive Mixture and in which the source power spectrograms are jointly modeled by a 3-valence (time/frequency/source) tensor. Our user-guided separation method produced competitive results at the 2010 Signal Separation Evaluation Campaign, with sufficient quality for real-world music editing applications.

  • Multichannel nonnegative tensor factorization with structured constraints for userguided audio source separation
    2011
    Co-Authors: Alexey Ozerov, Cédric Févotte, Raphaël Blouet, Jean-louis Durrieu
    Abstract:

    Separating multiple tracks from professionally produced music recordings (PPMRs) is still a challenging problem. We address this task with a user-guided approach in which the separation system is provided segmental information indicating the time activations of the particular instruments to separate. This information may typically be retrieved from manual annotation. We use a so-called multichannel nonnegative tensor factorization (NTF) model, in which the original sources are observed through a multichannel Convolutive Mixture and in which the source power spectrograms are jointly modeled by a 3-valence (time/frequency/source) tensor. Our user-guided separation method produced competitive results at the 2010 Signal Separation Evaluation Campaign, with sufficient quality for real-world music editing applications. Index Terms — Audio source separation, user-guided, nonnegative tensor factorization, generalized expectation maximization

  • multichannel nonnegative matrix factorization in Convolutive Mixtures for audio source separation
    IEEE Transactions on Audio Speech and Language Processing, 2010
    Co-Authors: Alexey Ozerov, Cédric Févotte
    Abstract:

    We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined Convolutive Mixture of source signals. We work in the short-time Fourier transform (STFT) domain, where convolution is routinely approximated as linear instantaneous mixing in each frequency band. Each source STFT is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization (EM) algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms are applied to stereo audio source separation in various settings, covering blind and supervised separation, music and speech sources, synthetic instantaneous and Convolutive Mixtures, as well as professionally produced music recordings. Our EM method produces competitive results with respect to state-of-the-art as illustrated on two tasks from the international Signal Separation Evaluation Campaign (SiSEC 2008).

  • C.: Multichannel nonnegative matrix factorization in Convolutive Mixtures for audio source separation
    2010
    Co-Authors: Alexey Ozerov, Cédric Févotte
    Abstract:

    We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly underdetermined Convolutive Mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance. Index Terms — Multichannel audio, nonnegative matrix factorization, nonnegative tensor factorization, underdetermined Convolutive blind source separation. 1

  • multichannel nonnegative matrix factorization in Convolutive Mixtures with application to blind audio source separation
    International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Alexey Ozerov, Cédric Févotte
    Abstract:

    We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly under-determined Convolutive Mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance.

Eric Moreau - One of the best experts on this subject based on the ideXlab platform.

  • 1A General Algebraic Algorithm for Blind Extraction of one Source in a MIMO Convolutive Mixture
    2015
    Co-Authors: Christophe De Luigi, Member Ieee, Marc Castella, Eric Moreau
    Abstract:

    Abstract—The paper deals with the problem of blind source extraction from a MIMO Convolutive Mixture. We define a new criterion for source extraction which uses higher-order contrast functions based on so called reference signals. It generalizes existing reference-based contrasts. In order to optimize the new criterion, we propose a general algebraic algorithm based on best rank-1 tensor approximation. Computer simulations illustrate the good behavior and the interest of our algorithm in comparison with other approaches

  • IEEE TRANSACTION ON SIGNAL PROCESSING (ACCEPTED MANUSCRIPT) 1 Generalized Identifiability Conditions for Blind Convolutive MIMO Separation
    2013
    Co-Authors: Marc Castella, Eric Moreau, Senior Member
    Abstract:

    Abstract—This paper deals with the problem of source separation in the case where the output of a multivariate Convolutive Mixture is observed: we propose novel and generalized conditions for the blind identifiability of a separating system. The results are based on higher-order statistics and are valid in the case of stationary but not necessarily i.i.d. signals. In particular, we extend recent results based on second-order statistics only. The approach relies on the use of so called reference signals. Our new results also show that only weak conditions are required on the reference signals: this is illustrated by simulations and opens up the possibility of developing new methods. Index Terms—Higher order statistics, MIMO Convolutive Mixtures, Blind source separation, MIMO identification, Contrast functions, Independent Component Analysis, Reference system, Semi-blind method

  • separation de sources en convolutif basee sur la diagonalisation simultanee de tenseurs
    21° Colloque GRETSI 2007 ; p. 1293-1296, 2007
    Co-Authors: Saloua Rhioui, Eric Moreau
    Abstract:

    We consider the blind source separation problem for Convolutive Mixture through the optimization of contrast function. We define a new contrast function using so-called reference signals. This generalizes a few existing results about the cumulants order and the source signals. An important point is that the proposed contrast is shown to be equivalent to a quadratic criterion of joint diagonalization of tensors using para-unitary Mixture.

  • sde sources en convolutif basee sur la diagonalisation simultanee de tenseurs
    2007
    Co-Authors: Saloua Rhioui, Eric Moreau
    Abstract:

    We consider the blind source separation problem for Convolutive Mixture through the optimization of contrast function. We deflne a new contrast function using so-called reference signals. This generalizes a few existing results about the cumulants order and the source signals. An important point is that the proposed contrast is shown to be equivalent to a quadratic criterion of joint diagonalization of tensors using para-unitary Mixture.

Simon Arberet - One of the best experts on this subject based on the ideXlab platform.

  • A WIDEBAND DOUBLY-SPARSE APPROACH FOR MITO SPARSE FILTER ESTIMATION
    2015
    Co-Authors: Simon Arberet, Prasad Sudhakar
    Abstract:

    We propose an approach for the estimation of sparse filters from a Convolutive Mixture of sources, exploiting the time-domain spar-sity of the mixing filters and the sparsity of the sources in the time-frequency (TF) domain. The proposed approach is based on a wide-band formulation of the cross-relation (CR) in the TF domain and on a framework including two steps: (a) a clustering step, to determine the TF points where the CR is valid; (b) a filter estimation step, to recover the set of filters associated with each source. We propose for the first time a method to blindly perform the clustering step (a) and we show that the proposed approach based on the wideband CR out-performs the narrowband approach and the GCC-PHAT approach by between 5 dB and 20 dB. Index Terms — Blind filter estimation, sparsity, convex optimi-sation, cross-relation, source separation 1

  • Reverberant Audio Source Separation via Sparse and Low-Rank Modeling
    IEEE Signal Processing Letters, 2014
    Co-Authors: Simon Arberet, Pierre Vandergheynst
    Abstract:

    The performance of audio source separation from underdetermined Convolutive Mixture assuming known mixing filters can be significantly improved by using an analysis sparse prior optimized by a reweighting l1 scheme and a wideband datafidelity term, as demonstrated by a recent article. In this letter, we show that the performance can be improved even more significantly by exploiting a low-rank prior on the source spectrograms.We present a new algorithm to estimate the sources based on i) an analysis sparse prior, ii) a reweighting scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a constrained form, and iv) a low-rank constraint on the source spectrograms. Evaluation on reverberant music Mixtures shows that the resulting algorithm improves state-of-the-art methods by more than 2 dB of signal-to-distortion ratio.

  • sparse reverberant audio source separation via reweighted analysis
    IEEE Transactions on Audio Speech and Language Processing, 2013
    Co-Authors: Simon Arberet, Jeanphilippe Thiran, Pierre Vandergheynst, Rafael E Carrillo, Yves Wiaux
    Abstract:

    We propose a novel algorithm for source signals estimation from an underdetermined Convolutive Mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant Mixture, assuming a synthesis sparse prior in the time-frequency domain and a narrowband approximation of the Convolutive mixing process. In this paper, we address the source estimation of Convolutive Mixtures with a new algorithm based on i) an analysis sparse prior, ii) a reweighting scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a constrained form. We show, through theoretical discussions and simulations, that this algorithm is particularly well suited for source separation of realistic reverberation Mixtures. Particularly, the proposed algorithm outperforms state-of-the-art methods on reverberant Mixtures of audio sources by more than 2 dB of signal-to-distortion ratio on the BSS Oracle dataset.

Yves Wiaux - One of the best experts on this subject based on the ideXlab platform.

  • sparse reverberant audio source separation via reweighted analysis
    IEEE Transactions on Audio Speech and Language Processing, 2013
    Co-Authors: Simon Arberet, Jeanphilippe Thiran, Pierre Vandergheynst, Rafael E Carrillo, Yves Wiaux
    Abstract:

    We propose a novel algorithm for source signals estimation from an underdetermined Convolutive Mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant Mixture, assuming a synthesis sparse prior in the time-frequency domain and a narrowband approximation of the Convolutive mixing process. In this paper, we address the source estimation of Convolutive Mixtures with a new algorithm based on i) an analysis sparse prior, ii) a reweighting scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a constrained form. We show, through theoretical discussions and simulations, that this algorithm is particularly well suited for source separation of realistic reverberation Mixtures. Particularly, the proposed algorithm outperforms state-of-the-art methods on reverberant Mixtures of audio sources by more than 2 dB of signal-to-distortion ratio on the BSS Oracle dataset.