P. C. Loizou, Speech enhancement: theory and practice, 2007.
DOI : 10.1201/9781420015836

E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, and M. E. Davies, Probabilistic modeling paradigms for audio source separation, Machine Audition: Principles, Algorithms and Systems, pp.162-185, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00544016

C. Févotte, N. Bertin, and J. Durrieu, Nonnegative matrix factorization with the Itakura-Saito divergence: With application to music analysis, Neural computation, vol.21, issue.3, pp.793-830, 2009.

N. Mohammadiha, P. Smaragdis, and A. Leijon, Supervised and unsupervised speech enhancement using nonnegative matrix factorization, IEEE Trans. Audio, Speech, Language Process, vol.21, issue.10, pp.2140-2151, 2013.
DOI : 10.1109/tasl.2013.2270369

URL : http://kth.diva-portal.org/smash/get/diva2:634165/FULLTEXT02

D. Wang and J. Chen, Supervised speech separation based on deep learning: An overview, IEEE Trans. Audio, Speech, Language Process, vol.26, issue.10, pp.1702-1726, 2018.

Y. Xu, J. Du, L. Dai, and C. Lee, A regression approach to speech enhancement based on deep neural networks, IEEE Trans. Audio, Speech, Language Process, vol.23, issue.1, pp.7-19, 2015.

F. Weninger, H. Erdogan, S. Watanabe, E. Vincent, J. Le-roux et al., Speech enhancement with LSTM recurrent neural networks and its application to noise-robust ASR, Proc. Int. Conf. Latent Variable Analysis and Signal Separation, pp.91-99, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01163493

D. P. Kingma and M. Welling, Auto-encoding variational Bayes, Proc. Int. Conf. Learning Representations (ICLR), 2014.

Y. Bando, M. Mimura, K. Itoyama, K. Yoshii, and T. Kawahara, Statistical speech enhancement based on probabilistic integration of variational autoencoder and non-negative matrix factorization, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp.716-720, 2018.

S. Leglaive, L. Girin, and R. Horaud, A variance modeling framework based on variational autoencoders for speech enhancement, Proc. IEEE Int. Workshop Machine Learning Signal Process. (MLSP), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01832826

K. Sekiguchi, Y. Bando, K. Yoshii, and T. Kawahara, Bayesian multichannel speech enhancement with a deep speech prior, Proc. AsiaPacific Signal and Information Processing Association Annual Summit and Conference, pp.1233-1239, 2018.

S. Leglaive, L. Girin, and R. Horaud, Semi-supervised multichannel speech enhancement with variational autoencoders and non-negative matrix factorization, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2019.
URL : https://hal.archives-ouvertes.fr/hal-02005102

A. Liutkus and R. Badeau, Generalized Wiener filtering with fractional power spectrograms, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp.266-270, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01110028

U. Sim¸seklisim¸sekli, A. Liutkus, and A. T. Cemgil, Alpha-stable matrix factorization, IEEE Signal Process. Lett, vol.22, issue.12, pp.2289-2293, 2015.

A. Liutkus, D. Fitzgerald, and R. Badeau, Cauchy nonnegative matrix factorization, Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust. (WASPAA), pp.1-5, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01170924

K. Yoshii, K. Itoyama, and M. Goto, Student's t nonnegative matrix factorization and positive semidefinite tensor factorization for singlechannel audio source separation, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp.51-55, 2016.

E. E. Kuruoglu, Signal processing in alpha-stable noise environments: a least lp-norm approach, 1999.

S. Leglaive, U. Sim¸seklisim¸sekli, A. Liutkus, R. Badeau, and G. Richard, Alpha-stable multichannel audio source separation, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process, pp.576-580, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01416366

M. Fontaine, A. Liutkus, L. Girin, and R. Badeau, Explaining the parameterized Wiener filter with alpha-stable processes, Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust. (WASPAA), 2017.
URL : https://hal.archives-ouvertes.fr/hal-01548508

M. Fontaine, F. Stöter, A. Liutkus, U. Sim¸seklisim¸sekli, R. Serizel et al.,

. Badeau, Multichannel Audio Modeling with Elliptically Stable Tensor Decomposition, Proc. Int. Conf. Latent Variable Analysis and Signal Separation (LVA/ICA), 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-01766795

U. Sim¸seklisim¸sekli, H. Erdo?-gan, S. Leglaive, A. Liutkus, R. Badeau et al., Alpha-stable low-rank plus residual decomposition for speech enhancement, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2018.

G. C. Wei and M. A. Tanner, A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms, Journal of the American statistical Association, vol.85, issue.411, pp.699-704, 1990.

A. Liutkus, R. Badeau, and G. Richard, Gaussian processes for underdetermined source separation, IEEE Trans. Signal Process, vol.59, issue.7, pp.3155-3167, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00643951

G. Samorodnitsky and M. S. Taqqu, Stable non-Gaussian random processes: stochastic models with infinite variance, vol.1, 1994.

D. F. Andrews and C. L. Mallows, Scale mixtures of normal distributions, Journal of the Royal Statistical Society. Series B (Methodological), vol.36, issue.1, pp.99-102, 1974.

P. Magron, R. Badeau, and A. Liutkus, Lévy NMF for robust nonnegative source separation, Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust. (WASPAA), pp.259-263, 2017.

K. Chan and J. Ledolter, Monte Carlo EM estimation for time series models involving counts, Journal of the American Statistical Association, vol.90, issue.429, pp.242-252, 1995.

C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2005.

C. Févotte and J. Idier, Algorithms for nonnegative matrix factorization with the ?-divergence, Neural computation, vol.23, issue.9, pp.2421-2456, 2011.

D. R. Hunter and K. Lange, A tutorial on MM algorithms, The American Statistician, vol.58, issue.1, pp.30-37, 2004.

J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, D. S. Pallett et al., TIMIT acoustic phonetic continuous speech corpus, 1993.

J. Thiemann, N. Ito, and E. Vincent, The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings, Proc. Int. Cong. on Acoust, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00796707

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, Proc. Int. Conf. Learning Representations (ICLR), 2015.

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proc. Int. Conf. Artif. Intelligence and Stat, pp.249-256, 2010.

E. Vincent, R. Gribonval, and C. Févotte, Performance measurement in blind audio source separation, IEEE Trans. Audio, Speech, Language Process, vol.14, issue.4, pp.1462-1469, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00544230

A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp.749-752, 2001.

C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, An algorithm for intelligibility prediction of time-frequency weighted noisy speech, IEEE Trans. Audio, Speech, Language Process, vol.19, issue.7, pp.2125-2136, 2011.

S. Venkataramani, R. Higa, and P. Smaragdis, Performance based cost functions for end-to-end speech separation, Proc. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp.350-355, 2018.