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Bibliography

Major publications by the team in recent years
  • 1F. Bimbot, E. Deruty, G. Sargent, E. Vincent.

    System & Contrast : A Polymorphous Model of the Inner Organization of Structural Segments within Music Pieces, in: Music Perception, June 2016, vol. 33, no 5, pp. 631-661. [ DOI : 10.1525/mp.2016.33.5.631 ]

    https://hal.inria.fr/hal-01188244
  • 2M. Chafii.

    Study of a new multicarrier waveform with low PAPR, CentraleSupélec, October 2016.

    https://hal.archives-ouvertes.fr/tel-01399509
  • 3N. Duong, E. Vincent, R. Gribonval.

    Under-determined reverberant audio source separation using a full-rank spatial covariance model, in: IEEE Transactions on Audio, Speech and Language Processing, July 2010, vol. 18, no 7, pp. 1830–1840. [ DOI : 10.1109/TASL.2010.2050716 ]

    https://hal.inria.fr/inria-00541865
  • 4D. K. Hammond, P. Vandergheynst, R. Gribonval.

    Wavelets on graphs via spectral graph theory, in: Applied and Computational Harmonic Analysis, March 2011, vol. 30, no 2, pp. 129–150. [ DOI : 10.1016/j.acha.2010.04.005 ]

    https://hal.inria.fr/inria-00541855
  • 5S. Kitić, L. Albera, N. Bertin, R. Gribonval.

    Physics-driven inverse problems made tractable with cosparse regularization, in: IEEE Transactions on Signal Processing, January 2016, vol. 64, no 2, pp. 335-348. [ DOI : 10.1109/TSP.2015.2480045 ]

    https://hal.inria.fr/hal-01133087
  • 6S. Kitić.

    Cosparse regularization of physics-driven inverse problems, IRISA, Inria Rennes, November 2015.

    https://hal.archives-ouvertes.fr/tel-01237323
  • 7C. Louboutin, F. Bimbot.

    Modeling the multiscale structure of chord sequences using polytopic graphs, in: 18th International Society for Music Information Retrieval Conference, Suzhou, China, October 2017.

    https://hal.archives-ouvertes.fr/hal-01653455
  • 8S. Nam, M. E. Davies, M. Elad, R. Gribonval.

    The Cosparse Analysis Model and Algorithms, in: Applied and Computational Harmonic Analysis, 2013, vol. 34, no 1, pp. 30–56, Preprint available on arXiv since 24 Jun 2011. [ DOI : 10.1016/j.acha.2012.03.006 ]

    http://hal.inria.fr/inria-00602205
  • 9A. Ozerov, E. Vincent, F. Bimbot.

    A General Flexible Framework for the Handling of Prior Information in Audio Source Separation, in: IEEE Transactions on Audio, Speech and Language Processing, May 2012, vol. 20, no 4, pp. 1118 - 1133, 16.

    http://hal.inria.fr/hal-00626962
  • 10H. Peic Tukuljac, A. Deleforge, R. Gribonval.

    MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval, in: NIPS 2018 - Thirty-second Conference on Neural Information Processing Systems, Montréal, Canada, December 2018, pp. 1-11, https://arxiv.org/abs/1810.13338.

    https://hal.inria.fr/hal-01906385
  • 11E. Vincent, N. Bertin, R. Gribonval, F. Bimbot.

    From blind to guided audio source separation, in: IEEE Signal Processing Magazine, December 2013.

    http://hal.inria.fr/hal-00922378
Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 14N. Bertin, E. Camberlein, R. Lebarbenchon, E. Vincent, S. Sivasankaran, I. Illina, F. Bimbot.

    VoiceHome-2, an extended corpus for multichannel speech processing in real homes, in: Speech Communication, January 2019, vol. 106, pp. 68-78. [ DOI : 10.1016/j.specom.2018.11.002 ]

    https://hal.inria.fr/hal-01923108
  • 15E. Byrne, A. Chatalic, R. Gribonval, P. Schniter.

    Sketched Clustering via Hybrid Approximate Message Passing, in: IEEE Transactions on Signal Processing, September 2019, vol. 67, no 17, pp. 4556-4569, https://arxiv.org/abs/1712.02849. [ DOI : 10.1109/TSP.2019.2924585 ]

    https://hal.inria.fr/hal-01991231
  • 16J. E. Cohen, N. Gillis.

    Identifiability of Complete Dictionary Learning, in: SIAM Journal on Mathematics of Data Science, 2019, vol. 1, no 3, pp. 518–536, https://arxiv.org/abs/1808.08765. [ DOI : 10.1137/18M1233339 ]

    https://hal.archives-ouvertes.fr/hal-02183578
  • 17C. Cury, P. Maurel, R. Gribonval, C. Barillot.

    A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction, in: Frontiers in Neuroscience, January 2020. [ DOI : 10.3389/fnins.2019.01451 ]

    https://www.hal.inserm.fr/inserm-02090676
  • 18A. Deleforge, D. Di Carlo, M. Strauss, R. Serizel, L. Marcenaro.

    Audio-Based Search and Rescue with a Drone: Highlights from the IEEE Signal Processing Cup 2019 Student Competition, in: IEEE Signal Processing Magazine, September 2019, vol. 36, no 5, pp. 138-144, https://arxiv.org/abs/1907.04655. [ DOI : 10.1109/MSP.2019.2924687 ]

    https://hal.archives-ouvertes.fr/hal-02161897
  • 19I. Dokmanić, R. Gribonval.

    Concentration of the Frobenius norm of generalized matrix inverses, in: SIAM Journal on Matrix Analysis and Applications, 2019, vol. 40, no 1, pp. 92–121, https://arxiv.org/abs/1810.07921 - Revised/condensed/renamed version of preprint "Beyond Moore-Penrose Part II: The Sparse Pseudoinverse", forthcoming. [ DOI : 10.1137/17M1145409 ]

    https://hal.inria.fr/hal-01897046
  • 20S. Foucart, R. Gribonval, L. Jacques, H. Rauhut.

    Jointly Low-Rank and Bisparse Recovery: Questions and Partial Answers, in: Analysis and Applications, 2020, vol. 18, no 01, pp. 25–48, https://arxiv.org/abs/1902.04731. [ DOI : 10.1142/S0219530519410094 ]

    https://hal.inria.fr/hal-02062891
  • 21C. Fraga Dantas, R. Gribonval.

    Stable safe screening and structured dictionaries for faster L1 regularization, in: IEEE Transactions on Signal Processing, July 2019, vol. 67, no 14, pp. 3756-3769, https://arxiv.org/abs/1812.06635. [ DOI : 10.1109/TSP.2019.2919404 ]

    https://hal.inria.fr/hal-01954261
  • 22R. Gribonval, M. Nikolova.

    On bayesian estimation and proximity operators, in: Applied and Computational Harmonic Analysis, 2019, pp. 1-25, https://arxiv.org/abs/1807.04021, forthcoming. [ DOI : 10.1016/j.acha.2019.07.002 ]

    https://hal.inria.fr/hal-01835108

Invited Conferences

  • 23R. Gribonval, G. Kutyniok, M. Nielsen, F. Voigtlaender.

    Approximation spaces of deep neural networks, in: SMAI 2019 - 9ème Biennale des Mathématiques Appliquées et Industrielles, Guidel, France, May 2019, 1 p.

    https://hal.inria.fr/hal-02127179
  • 24V. Schellekens, A. Chatalic, F. Houssiau, Y.-A. De Montjoye, L. Jacques, R. Gribonval.

    Differentially Private Compressive k-Means, in: ICASSP 2019 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, IEEE, May 2019, pp. 7933-7937. [ DOI : 10.1109/ICASSP.2019.8682829 ]

    https://hal.inria.fr/hal-02060208

International Conferences with Proceedings

  • 25A. Chatalic, N. Keriven, R. Gribonval.

    Projections aléatoires pour l'apprentissage compressif, in: GRETSI 2019 − XXVIIème Colloque francophone de traitement du signal et des images, Lille, France, August 2019, pp. 1-4.

    https://hal.inria.fr/hal-02154803
  • 26J. E. Cohen, N. Gillis.

    Nonnegative Low-rank Sparse Component Analysis, in: ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, IEEE, May 2019, pp. 8226-8230. [ DOI : 10.1109/ICASSP.2019.8682188 ]

    https://hal.archives-ouvertes.fr/hal-02201471
  • 27C. F. Dantas, J. E. Cohen, R. Gribonval.

    Hyperspectral Image Denoising using Dictionary Learning, in: WHISPERS 2019 - 10th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Amsterdam, Netherlands, September 2019, pp. 1-5.

    https://hal.inria.fr/hal-02175630
  • 28C. F. Dantas, J. E. Cohen, R. Gribonval.

    Learning Tensor-structured Dictionaries with Application to Hyperspectral Image Denoising, in: EUSIPCO 2019 - 27th European Signal Processing Conference, A Coruña, Spain, September 2019, pp. 1-5.

    https://hal.inria.fr/hal-02126782
  • 29D. Di Carlo, A. Deleforge, N. Bertin.

    Mirage: 2D Source Localization Using Microphone Pair Augmentation with Echoes, in: ICASSP 2019 - IEEE International Conference on Acoustic, Speech Signal Processing, Brighton, United Kingdom, IEEE, May 2019, pp. 775-779, https://arxiv.org/abs/1906.08968. [ DOI : 10.1109/ICASSP.2019.8683534 ]

    https://hal.archives-ouvertes.fr/hal-02160940
  • 30C. Elvira, R. Gribonval, C. Herzet, C. Soussen.

    Uniform k-step recovery with CMF dictionaries, in: SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations, Toulouse, France, July 2019, pp. 1-2.

    https://hal.inria.fr/hal-02157561
  • 31C. Elvira, R. Gribonval, C. Soussen, C. Herzet.

    OMP and continuous dictionaries: Is k-step recovery possible ?, in: ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, IEEE, May 2019, pp. 1-5. [ DOI : 10.1109/ICASSP.2019.8683617 ]

    https://hal.archives-ouvertes.fr/hal-02049486
  • 32V. Gillot, F. Bimbot.

    Polytopic reconfiguration: a graph-based scheme for the multiscale transformation of music segments and its perceptual assessment, in: SMC 2019 - 16th Sound & Music Computing Conference, Malaga, Spain, May 2019, pp. 1-8.

    https://hal.archives-ouvertes.fr/hal-02132955
  • 33S. Gupta, R. Gribonval, L. Daudet, I. Dokmanić.

    Don't take it lightly: Phasing optical random projections with unknown operators, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, pp. 1-13, https://arxiv.org/abs/1907.01703.

    https://hal.inria.fr/hal-02342280
  • 34M. Hafsati, N. Epain, R. Gribonval, N. Bertin.

    Sound source separation in the higher order ambisonics domain, in: DAFx 2019 - 22nd International Conference on Digital Audio Effects, Birmingham, United Kingdom, September 2019, pp. 1-7.

    https://hal.inria.fr/hal-02161949
  • 35V. Schellekens, A. Chatalic, F. Houssiau, Y.-A. De Montjoye, L. Jacques, R. Gribonval.

    Compressive k-Means with Differential Privacy, in: SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations, Toulouse, France, July 2019, pp. 1-2.

    https://hal.inria.fr/hal-02154820

National Conferences with Proceedings

  • 36C. Elvira, R. Gribonval, C. Soussen, C. Herzet.

    Identification de supports en k étapes avec OMP pour les dictionnaires continus, in: GRETSI 2019 - XXVIIème Colloque francophone de traitement du signal et des images, Lille, France, August 2019, pp. 1-4.

    https://hal.inria.fr/hal-02157571

Conferences without Proceedings

  • 37A. Ang, J. E. Cohen, N. Gillis.

    Accelerating Approximate Nonnegative Canonical Polyadic Decomposition using Extrapolation, in: GRETSI 2019 - XXVIIème Colloque francophone de traitement du signal et des images, Lille, France, August 2019, pp. 1-4.

    https://hal.archives-ouvertes.fr/hal-02143969
  • 38C. Cury, P. Maurel, R. Gribonval, C. Barillot.

    Can we learn from coupling EEG-fMRI to enhance neuro-feedback in EEG only?, in: OHBM 2019 - Annual Meeting Organization for Human Brain Mapping, Rome, Italy, June 2019, 1 p.

    https://www.hal.inserm.fr/inserm-02074623
  • 39C. Cury, P. Maurel, G. Lioi, R. Gribonval, C. Barillot.

    Learning bi-modal EEG-fMRI neurofeedback to improve neurofeedback in EEG only, in: Real-Time Functional Imaging and Neurofeedback, Maastricht, Netherlands, December 2019, pp. 1-2. [ DOI : 10.1101/599589 ]

    https://www.hal.inserm.fr/inserm-02368720
  • 40A. Lorente Mur, M. Ochoa, J. E. Cohen, X. Intes, N. Ducros.

    Handling negative patterns for fast single-pixel lifetime imaging, in: 2019 - Molecular-Guided Surgery: Molecules, Devices, and Applications V, San Francisco, United States, SPIE, February 2019, vol. 10862, pp. 1-10. [ DOI : 10.1117/12.2511123 ]

    https://hal.archives-ouvertes.fr/hal-02017598
  • 41P. Stock, B. Graham, R. Gribonval, H. Jégou.

    Equi-normalization of Neural Networks, in: ICLR 2019 - Seventh International Conference on Learning Representations, New Orleans, United States, May 2019, pp. 1-20, https://arxiv.org/abs/1902.10416.

    https://hal.archives-ouvertes.fr/hal-02050408
  • 42P. Stock, A. Joulin, R. Gribonval, B. Graham, H. Jégou.

    And the Bit Goes Down: Revisiting the Quantization of Neural Networks, in: ICLR 2020 - Eighth International Conference on Learning Representations, Addis-Abeba, Ethiopia, February 2020, pp. 1-11.

    https://hal.archives-ouvertes.fr/hal-02434572
  • 43T. Vayer, R. Flamary, R. Tavenard, L. Chapel, N. Courty.

    Sliced Gromov-Wasserstein, in: NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Vancouver, Canada, December 2019, vol. 32, https://arxiv.org/abs/1905.10124.

    https://hal.archives-ouvertes.fr/hal-02174309

Scientific Books (or Scientific Book chapters)

  • 44D. K. Hammond, P. Vandergheynst, R. Gribonval.

    The Spectral Graph Wavelet Transform: Fundamental Theory and Fast Computation, in: Vertex-Frequency Analysis of Graph Signals, L. Stanković, E. Sejdić (editors), Signals and Communication Technology, Springer International Publishing, December 2019, pp. 141-175. [ DOI : 10.1007/978-3-030-03574-7_3 ]

    https://hal.inria.fr/hal-01943589

Internal Reports

  • 45B. Caramiaux, F. Lotte, J. Geurts, G. Amato, M. Behrmann, F. Bimbot, F. Falchi, A. Garcia, J. Gibert, G. Gravier, H. Holken, H. Koenitz, S. Lefebvre, A. Liutkus, A. Perkis, R. Redondo, E. Turrin, T. Viéville, E. Vincent.

    AI in the media and creative industries, New European Media (NEM), April 2019, pp. 1-35, https://arxiv.org/abs/1905.04175.

    https://hal.inria.fr/hal-02125504

Software

  • 46C. F. Dantas, R. Gribonval.

    Stable Screening - Python code, June 2019,

    [ SWH-ID : swh:1:dir:3ccb3fde105b05aee192367fb5e07e192e3b6774 ]    , Software.

    https://hal.inria.fr/hal-02129219
  • 47M. Kowalski, E. Vincent, R. Gribonval.

    Underdetermined Reverberant Source Separation, October 2019,

    [ SWH-ID : swh:1:dir:ec4ae097465d9ea51589537ea94b2ea50e8d134d ]    , Software.

    https://hal.archives-ouvertes.fr/hal-02309043

Other Publications

References in notes
  • 57A. Adler, V. Emiya, M. G. Jafari, M. Elad, R. Gribonval, M. D. Plumbley.

    Audio Inpainting, in: IEEE Transactions on Audio, Speech and Language Processing, March 2012, vol. 20, no 3, pp. 922 - 932. [ DOI : 10.1109/TASL.2011.2168211 ]

    http://hal.inria.fr/inria-00577079
  • 58F. Bimbot.

    Towards an Information-Theoretic Framework for Music Structure, 2016, pp. 167-168, Invited talk at Dagstuhl Seminar 16092 on Computational Music Structure Analysis (Ed. : M. Müller, E. Chew, J.P. Bello). [ DOI : 10.4230/DagRep.6.2.147 ]

    https://hal.archives-ouvertes.fr/hal-01421013
  • 59A. Bonnefoy, V. Emiya, L. Ralaivola, R. Gribonval.

    Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso, in: IEEE Transactions on Signal Processing, 2015, vol. 63, no 19, 20 p. [ DOI : 10.1109/TSP.2015.2447503 ]

    https://hal.archives-ouvertes.fr/hal-01084986
  • 60A. Bourrier.

    Compressed sensing and dimensionality reduction for unsupervised learning, Université Rennes 1, May 2014.

    https://tel.archives-ouvertes.fr/tel-01023030
  • 61E. Byrne, R. Gribonval, P. Schniter.

    Sketched Clustering via Hybrid Approximate Message Passing, in: Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, United States, October 2017.

    https://hal.inria.fr/hal-01650160
  • 62M. Chafii, J. Palicot, R. Gribonval.

    Dispositif de communication à modulation temps-fréquence adaptative, July 2016, no Numéro de demande : 1656806 ; Numéro de soumission : 1000356937.

    https://hal.inria.fr/hal-01375661
  • 63A. Chatalic, R. Gribonval, N. Keriven.

    Large-Scale High-Dimensional Clustering with Fast Sketching, in: ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Canada, IEEE, April 2018, pp. 4714-4718. [ DOI : 10.1109/ICASSP.2018.8461328 ]

    https://hal.inria.fr/hal-01701121
  • 64I. Dokmanić, R. Gribonval.

    Beyond Moore-Penrose Part I: Generalized Inverses that Minimize Matrix Norms, July 2017, working paper or preprint.

    https://hal.inria.fr/hal-01547183
  • 65I. Dokmanić, R. Parhizkar, A. Walther, Y. M. Lu, M. Vetterli.

    Acoustic echoes reveal room shape, in: Proceedings of the National Academy of Sciences, 2013, vol. 110, no 30, pp. 12186–12191.

    http://dx.doi.org/10.1073/pnas.1221464110
  • 66C. Elvira, R. Gribonval, C. Herzet, C. Soussen.

    A case of exact recovery with OMP using continuous dictionaries, in: CS 2018 - 9th International Conference on Curves and Surfaces, Arcachon, France, June 2018.

    https://hal.inria.fr/hal-01937532
  • 67C. FRAGA DANTAS, J. E. Cohen, R. Gribonval.

    Learning fast dictionaries for sparse representations using low-rank tensor decompositions, in: LVA/ICA 2018 - 14th International Conference on Latent Variable Analysis and Signal Separation, Guildford, United Kingdom, LNCS, Springer, July 2018, vol. 10891, pp. 456-466. [ DOI : 10.1007/978-3-319-93764-9_42 ]

    https://hal.inria.fr/hal-01709343
  • 68C. FRAGA DANTAS, R. Gribonval.

    Dynamic Screening with Approximate Dictionaries, in: XXVIème colloque GRETSI, Juan-les-Pins, France, September 2017.

    https://hal.inria.fr/hal-01598021
  • 69C. Gaultier, N. Bertin, S. Kitić, R. Gribonval.

    A modeling and algorithmic framework for (non)social (co)sparse audio restoration, November 2017, working paper or preprint.

    https://hal.inria.fr/hal-01649261
  • 70M. A. Gerzon.

    Periphony: With-Height Sound Reproduction, in: J. Audio Eng. Soc, 1973, vol. 21, no 1, pp. 2–10.

    http://www.aes.org/e-lib/browse.cfm?elib=2012
  • 71R. Gribonval, G. Blanchard, N. Keriven, Y. Traonmilin.

    Compressive Statistical Learning with Random Feature Moments, December 2017, Main novelties compared to version 1: improved concentration bounds, improved sketch sizes for compressive k-means and compressive GMM that now scale linearly with the ambient dimension.

    https://hal.inria.fr/hal-01544609
  • 72R. Gribonval.

    Should penalized least squares regression be interpreted as Maximum A Posteriori estimation?, in: IEEE Transactions on Signal Processing, May 2011, vol. 59, no 5, pp. 2405-2410. [ DOI : 10.1109/TSP.2011.2107908 ]

    http://hal.inria.fr/inria-00486840
  • 73H. Jain.

    Learning compact representations for large scale image search, Université Rennes 1, June 2018.

    https://tel.archives-ouvertes.fr/tel-01889405
  • 74N. Keriven.

    Sketching for large-scale learning of mixture models, Université Rennes 1, October 2017.

    https://tel.archives-ouvertes.fr/tel-01620815
  • 75L. Le Magoarou, R. Gribonval.

    Flexible Multi-layer Sparse Approximations of Matrices and Applications, in: IEEE Journal of Selected Topics in Signal Processing, June 2016. [ DOI : 10.1109/JSTSP.2016.2543461 ]

    https://hal.inria.fr/hal-01167948
  • 76L. Le Magoarou, R. Gribonval, N. Tremblay.

    Approximate fast graph Fourier transforms via multi-layer sparse approximations, in: IEEE transactions on Signal and Information Processing over Networks, June 2018, vol. 4, no 2, pp. 407–420, https://arxiv.org/abs/1612.04542. [ DOI : 10.1109/TSIPN.2017.2710619 ]

    https://hal.inria.fr/hal-01416110
  • 77R. Lebarbenchon, E. Camberlein, D. Di Carlo, C. Gaultier, A. Deleforge, N. Bertin.

    Evaluation of an Open-Source Implementation of the SRP-PHAT Algorithm within the 2018 Locata Challenge, in: LOCATA Challenge Workshop, a satellite event of IWAENC 2018, Tokyo, Japan, September 2018.

    https://hal.archives-ouvertes.fr/hal-02187964
  • 78N. Libermann, F. Bimbot, E. Vincent.

    Exploration de dépendances structurelles mélodiques par réseaux de neurones récurrents, in: JIM 2018 - Journées d'Informatique Musicale, Amiens, France, May 2018, pp. 81-86.

    https://hal.archives-ouvertes.fr/hal-01791381
  • 79C. Louboutin, F. Bimbot.

    Description of Chord Progressions by Minimal Transport Graphs Using the System & Contrast Model, in: ICMC 2016 - 42nd International Computer Music Conference, Utrecht, Netherlands, September 2016.

    https://hal.archives-ouvertes.fr/hal-01421023
  • 80C. Louboutin, F. Bimbot.

    Modeling the multiscale structure of chord sequences using polytopic graphs, in: 18th International Society for Music Information Retrieval Conference, Suzhou, China, October 2017.

    https://hal.archives-ouvertes.fr/hal-01653455
  • 81C. Louboutin, F. Bimbot.

    Polytopic Graph of Latent Relations: A Multiscale Structure Model for Music Segments, in: 6th International Conference on Mathematics and Computation in Music (MCM 2017), Mexico City, Mexico, O. A. Agustín-Aquino, E. Lluis-Puebla, M. Montiel (editors), Lecture Notes in Computer Science book series, Springer, June 2017, vol. 10527.

    https://hal.archives-ouvertes.fr/hal-01653445
  • 82S. Noorzadeh, P. Maurel, T. Oberlin, R. Gribonval, C. Barillot.

    Multi-modal EEG and fMRI Source Estimation Using Sparse Constraints, in: MICCAI 2017 - 20th International Conference on Medical Image Computing and Computer Assisted Intervention, Quebec, Canada, September 2017. [ DOI : 10.1007/978-3-319-66182-7_51 ]

    https://hal.inria.fr/hal-01586495
  • 83H. Peic Tukuljac, A. Deleforge, R. Gribonval.

    MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval, in: NIPS 2018 - Thirty-second Conference on Neural Information Processing Systems, Montréal, Canada, December 2018, pp. 1-11, https://arxiv.org/abs/1810.13338.

    https://hal.inria.fr/hal-01906385
  • 84Y. Salaün, E. Vincent, N. Bertin, N. Souviraà-Labastie, X. Jaureguiberry, D. Tran, F. Bimbot.

    The Flexible Audio Source Separation Toolbox Version 2.0, May 2014, ICASSP.

    https://hal.inria.fr/hal-00957412
  • 85R. Scheibler, D. Di Carlo, A. Deleforge, I. Dokmanić.

    Separake: Source Separation with a Little Help From Echoes, in: ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Canada, April 2018.

    https://hal.inria.fr/hal-01909531


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