E. Vincent, T. Virtanen, and S. Gannot, Audio Source Separation and Speech Enhancement, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01881431

A. A. Nugraha, A. Liutkus, and E. Vincent, Multichannel audio source separation with deep neural networks, IEEE/ACM Trans. ASLP, vol.24, issue.9, pp.1652-1664, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01163369

J. Heymann, L. Drude, and R. Haeb-umbach, A generic neural acoustic beamforming architecture for robust multi-channel speech processing, Computer Speech & Language, vol.46, pp.374-385, 2017.

T. N. Sainath, R. J. Weiss, K. W. Wilson, B. Li, A. Narayanan et al., Multichannel signal processing with deep neural networks for automatic speech recognition, IEEE/ACM Trans. ASLP, vol.25, issue.5, pp.965-979, 2017.

E. Vincent, S. Watanabe, A. A. Nugraha, J. Barker, and R. Marxer, An analysis of environment, microphone and data simulation mismatches in robust speech recognition, Computer Speech & Language, vol.46, pp.535-557, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01399180

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 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 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. APSIPA, 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 ICASSP, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02005102

S. Leglaive, U. Im?ekli, A. Liutkus, L. Girin, and R. Horaud, Speech enhancement with variational autoencoders and alpha-stable distributions, Proc. IEEE ICASSP, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02005106

D. P. Kingma and M. Welling, Auto-encoding variational Bayes, Proc. ICLR, 2014.

D. D. Lee and H. S. Seung, Learning the parts of objects by nonnegative matrix factorization, Nature, vol.401, issue.6755, pp.788-791, 1999.

C. Boutsidis and E. Gallopoulos, SVD based initialization: A head start for nonnegative matrix factorization, Pattern Recognition, vol.41, issue.4, pp.1350-1362, 2008.

R. A. Fisher, Applications of "Student's" distribution, Metron, vol.5, issue.3, pp.90-104, 1925.

R. Martin and C. Breithaupt, Speech enhancement in the DFT domain using Laplacian speech priors, Proc. IWAENC, vol.3, pp.87-90, 2003.

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

S. Leglaive, U. Im?ekli, A. Liutkus, R. Badeau, and G. Richard, Alphastable multichannel audio source separation, Proc. IEEE ICASSP, pp.576-580, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01416366

M. Fontaine, F. Stöter, A. Liutkus, U. Im?ekli, R. Serizel et al., Multichannel audio modeling with elliptically stable tensor decomposition, Proc. LVA/ICA, pp.13-23, 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-01766795

U. Im?ekli, H. Erdogan, S. Leglaive, A. Liutkus, R. Badeau et al., Alpha-stable low-rank plus residual decomposition for speech enhancement, Proc. IEEE ICASSP, 2018.

M. Fontaine, A. Liutkus, L. Girin, and R. Badeau, Explaining the parameterized Wiener filter with alpha-stable processes, Proc. WASPAA, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01548508

A. Liutkus, D. Fitzgerald, and R. Badeau, Cauchy nonnegative matrix factorization, Proc. WASPAA, pp.1-5, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01170924

K. Kitamura, Y. Bando, K. Itoyama, and K. Yoshii, Student's t multichannel nonnegative matrix factorization for blind source separation, Proc. IWAENC, pp.1-5, 2016.

D. Kitamura, S. Mogami, Y. Mitsui, N. Takamune, H. Saruwatari et al., Generalized independent low-rank matrix analysis using heavy-tailed distributions for blind source separation, EURASIP J. on Adv. in Signal Process, vol.2018, issue.1, pp.1-25, 2018.

M. Lombardi and D. Veredas, Indirect estimation of elliptical stable distributions, Computational Statistics & Data Analysis, vol.53, issue.6, pp.2309-2324, 2009.

S. J. Press, Multivariate stable distributions, Journal of Multivariate Analysis, vol.2, issue.4, pp.444-462, 1972.

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

N. Q. Duong, E. Vincent, and R. Gribonval, Under-determined reverberant audio source separation using a full-rank spatial covariance model, IEEE Trans. ASLP, vol.18, issue.7, pp.1830-1840, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00435807

D. Fitzgerald, A. Liutkus, and R. Badeau, Projection-based demixing of spatial audio, IEEE/ACM Trans. ASLP, vol.24, issue.9, pp.1556-1568, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01260588

S. Kullback and R. A. Leibler, On information and sufficiency, Ann. Math. Statist, vol.22, issue.1, pp.79-86, 1951.

A. Liutkus and R. Badeau, Generalized Wiener filtering with fractional power spectrograms, Proc. IEEE ICASSP, pp.266-270, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01110028

A. Ozerov and C. Févotte, Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation, IEEE Trans. ASLP, vol.18, issue.3, pp.550-563, 2009.

E. Vincent, H. Sawada, P. Bofill, S. Makino, and J. P. Rosca, First stereo audio source separation evaluation campaign: Data, algorithms and results, Proc. ICA, pp.552-559, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00544199

&. Itu-t, 862 : Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs, 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. ASLP, vol.19, issue.7, pp.2125-2136, 2011.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, Proc. ICLR, 2015.

R. Pascanu, T. Mikolov, and Y. Bengio, On the difficulty of training recurrent neural networks, Proc. ICML, pp.1310-1318, 2013.

T. Salimans and D. P. Kingma, Weight normalization: A simple reparameterization to accelerate training of deep neural networks, Proc. NIPS, pp.901-909, 2016.

C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther, Ladder variational autoencoders, Proc. NIPS, pp.3738-3746, 2016.