M. Berouti, R. Schwartz, and J. Makhoul, Enhancement of speech corrupted by acoustic noise, ICASSP '79. IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.208-211, 1979.
DOI : 10.1109/ICASSP.1979.1170788

Z. Goh, K. Tan, and B. T. Tan, Postprocessing method for suppressing musical noise generated by spectral subtraction, IEEE Trans. Speech Audio Process, vol.6, issue.3, pp.287-292, 1998.

Y. Uemura, Y. Takahashi, H. Saruwatari, K. S. , and K. Kondo, Automatic optimization scheme of spectral subtraction based on musical noise assessment via higher-order statistics, Proc. IWAENC, 2008.

Y. Ephraim and D. Malah, Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.32, issue.6, pp.1109-1121, 1984.
DOI : 10.1109/TASSP.1984.1164453

O. Cappé, Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor, IEEE Transactions on Speech and Audio Processing, vol.2, issue.2, pp.345-349, 1994.
DOI : 10.1109/89.279283

C. Lu, Reduction of musical residual noise for speech enhancement using masking properties and optimal smoothing, Pattern Recognition Letters, vol.28, issue.11, pp.1300-1306, 2007.
DOI : 10.1016/j.patrec.2007.03.001

G. Yu, S. Mallat, and E. Bacry, Audio Denoising by Time-Frequency Block Thresholding, IEEE Transactions on Signal Processing, vol.56, issue.5, pp.1830-1839, 2008.
DOI : 10.1109/TSP.2007.912893

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.2084

S. and B. Jebara, A Perceptual Approach to Reduce Musical Noise Phenomenon with Wiener Denoising Technique, 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006.
DOI : 10.1109/ICASSP.2006.1660587

S. Araki, S. Makino, H. Sawada, and R. Mukai, Reducing Musical Noise by a Fine-shift Overlap-add Method Applied to Source Separation Using a Time-frequency Mask, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., pp.81-84, 2005.
DOI : 10.1109/ICASSP.2005.1415651

T. Esch and P. Vary, Efficient musical noise suppression for speech enhancement system, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.4409-4412, 2009.
DOI : 10.1109/ICASSP.2009.4960607

Y. Uemura, Y. Takahashi, H. Saruwatari, K. Shikano, and K. Kondo, Musical noise generation analysis for noise reduction methods based on spectral subtraction and MMSE STSA estimation, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.4433-4436, 2009.
DOI : 10.1109/ICASSP.2009.4960613

C. Févotte, B. Torrésani, L. Daudet, and S. J. , Sparse Linear Regression With Structured Priors and Application to Denoising of Musical Audio, IEEE Transactions on Audio, Speech, and Language Processing, vol.16, issue.1, pp.174-185, 2008.
DOI : 10.1109/TASL.2007.909290

M. D. Plumbley, T. Blumensath, L. Daudet, R. Gribonval, and M. E. Davies, Sparse Representations in Audio and Music: From Coding to Source Separation, Proc. IEEE, pp.995-1005, 2010.
DOI : 10.1109/JPROC.2009.2030345

URL : https://hal.archives-ouvertes.fr/inria-00489524

T. Inoue, H. Saruwatari, K. Shikano, and K. Kondo, Theoretical analysis of musical noise in Wiener filter via higher-order statistics, Proc. APSIPA, 2010.

N. Derakhshan, M. Rahmani, A. Akbari, and A. Ayatollahi, An objective measure for the musical noise assessment in noise reduction systems, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.4429-4432, 2009.
DOI : 10.1109/ICASSP.2009.4960612

P. Flandrin, Time–Frequency Filtering Based on Spectrogram Zeros, IEEE Signal Processing Letters, vol.22, issue.11, pp.2137-2141, 1979.
DOI : 10.1109/LSP.2015.2463093

P. Flandrin, Time-frequency/Time-scale Analysis, 1998.

N. Sturmel and L. Daudet, Signal reconstruction from stft magnitude: a state of the art, Proc. DAFx-11, pp.375-386, 2011.

S. Meignen, T. Oberlin, P. Depalle, P. Flandrin, and S. Mclaughlin, Adaptive multimode signal reconstruction from time???frequency representations, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.8, issue.2065, 2016.
DOI : 10.1023/A:1008097225773

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792411

S. Araki, F. Nesta, E. Vincent, Z. Ek-koldovsk-`-koldovsk-`-y, G. Nolte et al., The 2011 Signal Separation Evaluation Campaign (SiSEC2011): - Audio Source Separation -, Proc. LVA/ICA, pp.414-422, 2012.
DOI : 10.1109/JSTSP.2011.2158801

URL : https://hal.archives-ouvertes.fr/hal-00655394

V. Emiya, E. Vincent, N. Harlander, and V. Hohmann, Subjective and Objective Quality Assessment of Audio Source Separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.19, issue.7, pp.2046-2057, 2011.
DOI : 10.1109/TASL.2011.2109381

URL : https://hal.archives-ouvertes.fr/inria-00567152

M. G. Jafari and M. D. Plumbley, Speech denoising based on a greedy adaptive dictionary algorithm, Proc. EUSIPCO. IEEE, pp.1423-1426, 2009.

C. Kereliuk and P. Depalle, Sparse atomic modeling of audio: A review, Proc. DAFx-11, 2011.

Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp.40-44, 1993.
DOI : 10.1109/ACSSC.1993.342465

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.348.5735

A. Adler, V. Emiya, M. G. 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