2014 - IEEE Fellow For contributions to the theory and applications of sparse signal processing
Rémi Gribonval mainly investigates Sparse approximation, Source separation, Artificial intelligence, Algorithm and Pattern recognition. His studies deal with areas such as Discrete mathematics, Combinatorics, Inpainting, Greedy algorithm and Sparse matrix as well as Sparse approximation. His Source separation study combines topics from a wide range of disciplines, such as Audio signal processing, Estimation theory and Blind signal separation.
His Wavelet study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Envelope detector, bridging the gap between disciplines. His Algorithm research is mostly focused on the topic Matching pursuit. His work in the fields of Continuous wavelet transform overlaps with other areas such as Gaussian process.
Rémi Gribonval spends much of his time researching Artificial intelligence, Algorithm, Sparse approximation, Pattern recognition and Source separation. His work in Algorithm covers topics such as Mathematical optimization which are related to areas like Inverse problem and Convex optimization. Rémi Gribonval combines subjects such as Matching pursuit, Sparse matrix, Theoretical computer science and Signal processing with his study of Sparse approximation.
His work on Mixture model as part of general Pattern recognition study is frequently connected to Gaussian process, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His Source separation research is multidisciplinary, incorporating elements of Audio signal processing, Covariance function and Blind signal separation. His Blind signal separation research incorporates elements of Estimation theory and Independent component analysis.
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Machine learning, Estimator and Discrete mathematics. His studies in Artificial intelligence integrate themes in fields like User interface and Pattern recognition. Rémi Gribonval focuses mostly in the field of Pattern recognition, narrowing it down to matters related to White noise and, in some cases, Speech recognition.
His Algorithm research includes elements of k-means clustering, Sparse matrix and Fourier transform. His Discrete mathematics study incorporates themes from Harmonic wavelet transform, Fractional Fourier transform and Monotone polygon. A large part of his Sparse approximation studies is devoted to K-SVD.
Rémi Gribonval spends much of his time researching Algorithm, Estimator, Quantization, Regularization and Contextual image classification. He interconnects Sparse matrix, k-means clustering, Cluster analysis and Operator in the investigation of issues within Algorithm. His Estimator study integrates concerns from other disciplines, such as Scale, Probability distribution, Outcome and Applied mathematics.
His work deals with themes such as Flow, Model compression and Noise, which intersect with Quantization. The study incorporates disciplines such as Domain, Optimization problem and Second-order cone programming in addition to Regularization. Contextual image classification is closely attributed to Compression in his study.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Performance measurement in blind audio source separation
E. Vincent;R. Gribonval;C. Fevotte.
IEEE Transactions on Audio, Speech, and Language Processing (2006)
Wavelets on graphs via spectral graph theory
David K. Hammond;Pierre Vandergheynst;Rémi Gribonval.
Applied and Computational Harmonic Analysis (2011)
Sparse representations in unions of bases
R. Gribonval;M. Nielsen.
IEEE Transactions on Information Theory (2003)
The Cosparse Analysis Model and Algorithms
Sangnam Nam;Mike E. Davies;Michael Elad;Rémi Gribonval.
Applied and Computational Harmonic Analysis (2013)
Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model
Ngoc Q K Duong;Emmanuel Vincent;Rémi Gribonval.
IEEE Transactions on Audio, Speech, and Language Processing (2010)
Harmonic decomposition of audio signals with matching pursuit
R. Gribonval;E. Bacry.
IEEE Transactions on Signal Processing (2003)
Fast matching pursuit with a multiscale dictionary of Gaussian chirps
IEEE Transactions on Signal Processing (2001)
Sparse Representations in Audio and Music: From Coding to Source Separation
Mark D Plumbley;Thomas Blumensath;Laurent Daudet;Remi Gribonval.
Proceedings of the IEEE (2010)
BSS_EVAL Toolbox User Guide -- Revision 2.0
Cédric Févotte;Rémi Gribonval;Emmanuel Vincent.
On the exponential convergence of matching pursuits in quasi-incoherent dictionaries
R. Gribonval;P. Vandergheynst.
IEEE Transactions on Information Theory (2006)
Profile was last updated on December 6th, 2021.
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