A. Adler, V. Emiya, M. Jafari, M. Elad, R. Gribonval et al., Audio Inpainting, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, issue.3, pp.922-932, 2012.
DOI : 10.1109/TASL.2011.2168211
URL : https://hal.archives-ouvertes.fr/inria-00577079

A. Janssen, R. Veldhuis, and L. Vries, Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.34, issue.2, pp.317-330, 1986.
DOI : 10.1109/TASSP.1986.1164824

S. Abel, J. O. Smith, and I. , Restoring a clipped signal, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, p.1745171748, 1991.
DOI : 10.1109/ICASSP.1991.150655

S. J. Godsill and P. J. Rayner, A Bayesian approach to the restoration of degraded audio signals, IEEE Transactions on Speech and Audio Processing, vol.3, issue.4, pp.267-278, 1995.
DOI : 10.1109/89.397091

S. Kiti´ckiti´c, L. Jacques, N. Madhu, M. P. Hopwood, A. Spriet et al., Consistent iterative hard thresholding for signal declipping, ICASSP -The 38th International Conference on Acoustics, Speech, and Signal Processing, 2013.

K. Siedenburg, M. Kowalski, and M. Dörfler, Audio declipping with social sparsity, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1577-1581, 2014.
DOI : 10.1109/ICASSP.2014.6853863
URL : https://hal.archives-ouvertes.fr/hal-01002998

M. J. Harvilla and R. M. Stern, Efficient audio declipping using regularized least squares, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.221-225, 2015.
DOI : 10.1109/ICASSP.2015.7177964

S. Kiti´ckiti´c, N. Bertin, and R. Gribonval, Sparsity and cosparsity for audio declipping: a flexible non-convex approach, The 12th International Conference on Latent Variable Analysis and Signal Separation, 2015.

C. ¸. Bilen, A. Ozerov, and P. Pérez, Audio declipping via nonnegative matrix factorization, 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2015.
DOI : 10.1109/WASPAA.2015.7336948
URL : https://hal.archives-ouvertes.fr/hal-01171813

X. Zhang and D. H. Brainard, Estimation of saturated pixel values in digital color imaging, Journal of the Optical Society of America A, vol.21, issue.12, pp.2301-2310, 2004.
DOI : 10.1364/JOSAA.21.002301

H. Mansour, R. Saab, P. Nasiopoulos, and R. Ward, Color image desaturation using sparse reconstruction, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010.
DOI : 10.1109/ICASSP.2010.5494984

A. Ozerov and C. Févotte, Multichannel Nonnegative Matrix Factorization in Convolutive Mixtures for Audio Source Separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.18, issue.3, pp.550-563, 2010.
DOI : 10.1109/TASL.2009.2031510

H. Sawada, S. Araki, and S. Makino, Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment, IEEE Transactions on Audio, Speech, and Language Processing, vol.19, issue.3, pp.516-527, 2011.
DOI : 10.1109/TASL.2010.2051355

C. Févotte, A. Ozerov15, ]. A. Ozerov, C. Févotte, R. Blouet et al., Notes on nonnegative tensor factorization of the spectrogram for audio source separation : statistical insights and towards self-clustering of the spatial cues Multichannel nonnegative tensor factorization with structured constraints for user-guided audio source separation, 7th International Symposium on Computer Music Modeling and Retrieval IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.257-260, 2010.

N. Q. Duong, E. Vincent, and R. Gribonval, Under-Determined Reverberant Audio Source Separation Using a Full-Rank Spatial Covariance Model, IEEE Transactions on Audio, Speech, and Language Processing, vol.18, issue.7, pp.1830-1840, 2010.
DOI : 10.1109/TASL.2010.2050716
URL : https://hal.archives-ouvertes.fr/inria-00435807

A. Liutkus, R. Badeau, and G. Richard, Low bitrate informed source separation of realistic mixtures, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013.
DOI : 10.1109/ICASSP.2013.6637610
URL : https://hal.archives-ouvertes.fr/hal-00945299

C. ¸. Bilen, A. Ozerov, and P. Pérez, Joint Audio Inpainting and Source Separation, The 12th International Conference on Latent Variable Analysis and Signal Separation, 2015.
DOI : 10.1007/978-3-319-22482-4_29
URL : https://hal.archives-ouvertes.fr/hal-01160438

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society. Series B (Methodological), vol.39, pp.1-38, 1977.

S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, 1993.

C. Févotte, N. Bertin, and J. L. Durrieu, Nonnegative Matrix Factorization with the Itakura-Saito Divergence: With Application to Music Analysis, Neural Computation, vol.14, issue.3, pp.793-830, 2009.
DOI : 10.1016/j.sigpro.2007.01.024

S. Arberet, A. Ozerov, N. Q. Duong, E. Vincent, R. Gribonval et al., Nonnegative matrix factorization and spatial covariance model for underdetermined reverberant audio source separation, 10th Int. Conf. on Information Sciences, Signal Proc. and their applications, pp.1-4, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00541436

A. Ozerov, E. Vincent, and F. Bimbot, A General Flexible Framework for the Handling of Prior Information in Audio Source Separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, issue.4, pp.1118-1133, 2012.
DOI : 10.1109/TASL.2011.2172425
URL : https://hal.archives-ouvertes.fr/inria-00536917

S. Araki, A. Ozerov, B. V. Gowreesunker, H. Sawada, F. J. Theis et al., The 2010 Signal Separation Evaluation Campaign (SiSEC2010): Audio Source Separation, Proc. Int. Conf. on Latent Variable Analysis and Signal Separation, pp.114-122, 2010.
DOI : 10.1007/978-3-642-15995-4_15
URL : https://hal.archives-ouvertes.fr/inria-00553385