Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Partnerships and Cooperations
Dissemination
Bibliography
XML PDF e-pub
PDF e-Pub


Bibliography

Major publications by the team in recent years
[1]
F. 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
[2]
M. Chafii.
Study of a new multicarrier waveform with low PAPR, CentraleSupélec, October 2016.
https://hal.archives-ouvertes.fr/tel-01399509
[3]
N. 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
[4]
D. 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
[5]
S. 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
[6]
S. Kitić.
Cosparse regularization of physics-driven inverse problems, IRISA, Inria Rennes, November 2015.
https://hal.archives-ouvertes.fr/tel-01237323
[7]
C. 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
[8]
S. 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
[9]
A. 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
[10]
H. 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
[11]
E. 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

[12]
C. Gaultier.
Design and evaluation of sparse models and algorithms for audio inverse problems, Université Rennes 1, January 2019.
https://tel.archives-ouvertes.fr/tel-02148598
[13]
C. Louboutin.
Multi-scale and multi-dimensional modelling of music structure using polytopic graphs, Université Rennes 1, March 2019.
https://tel.archives-ouvertes.fr/tel-02149728

Articles in International Peer-Reviewed Journals

[14]
N. 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
[15]
E. 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
[16]
J. 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
[17]
C. 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
[18]
A. 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
[19]
I. 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
[20]
S. 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
[21]
C. 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
[22]
R. 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

[23]
R. 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
[24]
V. 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

[25]
A. 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
[26]
J. 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
[27]
C. 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
[28]
C. 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
[29]
D. 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
[30]
C. 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
[31]
C. 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
[32]
V. 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
[33]
S. 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
[34]
M. 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
[35]
V. 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

[36]
C. 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

[37]
A. 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
[38]
C. 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
[39]
C. 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
[40]
A. 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
[41]
P. 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
[42]
P. 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
[43]
T. 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)

[44]
D. 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

[45]
B. 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

[46]
C. F. Dantas, R. Gribonval.
Stable Screening - Python code, June 2019,
[ SWH-ID : swh:1:dir:3ccb3fde105b05aee192367fb5e07e192e3b6774 ]
, Software.
https://hal.inria.fr/hal-02129219
[47]
M. 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

[48]
A. Ackaouy, N. Courty, E. Vallee, O. Commowick, C. Barillot, F. Galassi.
Unsupervised Domain Adaptation with Optimal Transport in multi-site segmentation of Multiple Sclerosis lesions from MRI data : Preprint, October 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02317028
[49]
B. B. Damodaran, R. Flamary, V. Seguy, N. Courty.
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images, July 2019, https://arxiv.org/abs/1810.01163 - Under Consideration at Computer Vision and Image Understanding. [ DOI : 10.01163 ]
https://hal.archives-ouvertes.fr/hal-02174320
[50]
C. F. Dantas, J. E. Cohen, R. Gribonval.
Tensor-structured Dictionaries for Hyperspectral Imaging, July 2019, pp. 1-2, SPARS 2019 - Signal Processing with Adaptive Sparse Structured Representations.
https://hal.inria.fr/hal-02169405
[51]
C. Elvira, R. Gribonval, C. Soussen, C. Herzet.
When does OMP achieve support recovery with continuous dictionaries?, April 2019, working paper or preprint.
https://hal.inria.fr/hal-02099464
[52]
C. Elvira, C. Herzet.
Safe Squeezing for Antisparse Coding, November 2019, working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02368134
[53]
R. Gribonval, G. Kutyniok, M. Nielsen, F. Voigtlaender.
Approximation spaces of deep neural networks, June 2019, https://arxiv.org/abs/1905.01208 - working paper or preprint.
https://hal.inria.fr/hal-02117139
[54]
R. Gribonval, M. Nikolova.
A characterization of proximity operators, November 2019, https://arxiv.org/abs/1807.04014 - working paper or preprint.
https://hal.inria.fr/hal-01835101
[55]
A. Lorente Mur, J. E. Cohen, N. Ducros.
Factorisation exacte pour la généralisation de motifs en imagerie monopixel, March 2019, 1 p, JIONC - 14ème Journées d'Imagerie Optique Non Conventionelle, Poster.
https://hal.archives-ouvertes.fr/hal-02170148
[56]
T. Vayer, L. Chapel, R. Flamary, R. Tavenard, N. Courty.
Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties, July 2019, https://arxiv.org/abs/1811.02834 - working paper or preprint.
https://hal.archives-ouvertes.fr/hal-02174316
References in notes
[57]
A. 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
[58]
F. 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
[59]
A. 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
[60]
A. Bourrier.
Compressed sensing and dimensionality reduction for unsupervised learning, Université Rennes 1, May 2014.
https://tel.archives-ouvertes.fr/tel-01023030
[61]
E. 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
[62]
M. 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
[63]
A. 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
[64]
I. 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
[65]
I. 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
[66]
C. 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
[67]
C. 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
[68]
C. 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
[69]
C. 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
[70]
M. 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
[71]
R. 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
[72]
R. 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
[73]
H. Jain.
Learning compact representations for large scale image search, Université Rennes 1, June 2018.
https://tel.archives-ouvertes.fr/tel-01889405
[74]
N. Keriven.
Sketching for large-scale learning of mixture models, Université Rennes 1, October 2017.
https://tel.archives-ouvertes.fr/tel-01620815
[75]
L. 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
[76]
L. 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
[77]
R. 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
[78]
N. 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
[79]
C. 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
[80]
C. 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
[81]
C. 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
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