S. Makino, H. Sawada, and T. Lee, Blind Speech Separation, 2007.
DOI : 10.1007/978-1-4020-6479-1

G. R. Naik and W. Wang, Blind Source Separation: Advances in Theory, Algorithms and Applications, 2014.
DOI : 10.1007/978-3-642-55016-4

E. Vincent, N. Bertin, R. Gribonval, and F. Bimbot, From Blind to Guided Audio Source Separation: How models and side information can improve the separation of sound, IEEE Signal Processing Magazine, vol.31, issue.3, pp.107-115, 2014.
DOI : 10.1109/MSP.2013.2297440

URL : https://hal.archives-ouvertes.fr/hal-00922378

L. Deng and D. Yu, Deep Learning: Methods and Applications, Foundations and Trends?? in Signal Processing, vol.7, issue.3-4, pp.3-4, 2014.
DOI : 10.1561/2000000039

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

Y. Tu, J. Du, Y. Xu, L. Dai, and C. Lee, Speech separation based on improved deep neural networks with dual outputs of speech features for both target and interfering speakers, The 9th International Symposium on Chinese Spoken Language Processing, pp.250-254
DOI : 10.1109/ISCSLP.2014.6936615

P. Huang, M. Kim, M. Hasegawa-johnson, and P. Smaragdis, Joint optimization of masks and deep recurrent neural networks for monaural source separation, IEEE/ACM Trans. ASLP, vol.23, issue.12, pp.2136-2147, 2015.

S. Araki, T. Hayashi, M. Delcroix, M. Fujimoto, K. Takeda et al., Exploring multi-channel features for denoisingautoencoder-based speech enhancement, Proc. IEEE ICASSP, pp.116-120, 2015.
DOI : 10.1109/icassp.2015.7177943

S. Uhlich, F. Giron, and Y. Mitsufuji, Deep neural network based instrument extraction from music, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2135-2139, 2015.
DOI : 10.1109/ICASSP.2015.7178348

Y. Wang and D. Wang, Towards scaling up classification-based speech separation, IEEE Trans. ASLP, vol.21, issue.7, pp.1381-1390, 2013.

]. Y. Jiang, D. Wang, R. Liu, and Z. Feng, Binaural Classification for Reverberant Speech Segregation Using Deep Neural Networks, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.22, issue.12, pp.16-2112, 2014.
DOI : 10.1109/TASLP.2014.2361023

F. Weninger, J. Le-roux, J. R. Hershey, and B. Schuller, Discriminatively trained recurrent neural networks for single-channel speech separation, 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp.577-581
DOI : 10.1109/GlobalSIP.2014.7032183

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

A. Narayanan and D. Wang, Improving robustness of deep neural network acoustic models via speech separation and joint adaptive training, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.23, issue.1, pp.92-101, 2015.
DOI : 10.1109/TASLP.2014.2372314

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4784988

Y. Wang and D. Wang, A deep neural network for time-domain signal reconstruction, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4390-4394, 2015.
DOI : 10.1109/ICASSP.2015.7178800

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-00541865

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/hal-00626962

T. Gerber, M. Dutasta, L. Girin, and C. Févotte, Professionallyproduced music separation guided by covers, Proc. ISMIR, pp.85-90, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00807027

A. Liutkus, D. Fitzgerald, Z. Rafii, B. Pardo, and L. Daudet, Kernel Additive Models for Source Separation, IEEE Transactions on Signal Processing, vol.62, issue.16, pp.4298-4310, 2014.
DOI : 10.1109/TSP.2014.2332434

URL : https://hal.archives-ouvertes.fr/hal-01011044

A. Liutkus, D. Fitzgerald, and Z. Rafii, Scalable audio separation with light Kernel Additive Modelling, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.76-80, 2015.
DOI : 10.1109/ICASSP.2015.7177935

URL : https://hal.archives-ouvertes.fr/hal-01114890

N. Ono, D. Kitamura, Z. Rafii, N. Ito, and A. Liutkus, The 2015 signal separation evaluation campaign (SiSEC2015), Proc. LVA/ICA, 2015.
DOI : 10.1007/978-3-319-22482-4_45

URL : https://hal.inria.fr/hal-01188725/document

A. A. Nugraha, A. Liutkus, and E. Vincent, Multichannel Audio Source Separation With Deep Neural Networks, IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.24, issue.9
DOI : 10.1109/TASLP.2016.2580946

URL : https://hal.archives-ouvertes.fr/hal-01163369

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, 2011.
DOI : 10.4018/978-1-61520-919-4.ch007

URL : https://hal.archives-ouvertes.fr/inria-00544016

S. Sivasankaran, A. A. Nugraha, E. Vincent, J. A. Morales-cordovilla, S. Dalmia et al., Robust ASR using neural network based speech enhancement and feature simulation, 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.482-489, 2015.
DOI : 10.1109/ASRU.2015.7404834

URL : https://hal.archives-ouvertes.fr/hal-01204553

X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier networks, Proc. AISTATS, pp.315-323, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00752497

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Research, vol.15, pp.1929-1958, 2014.

Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, Greedy layerwise training of deep networks, Proc. NIPS, pp.153-160, 2006.

M. D. Zeiler, ADADELTA: An adaptive learning rate method ArXiv e-prints, 2012.

E. Vincent, R. Gribonval, and C. Févotte, Performance measurement in blind audio source separation, IEEE Transactions on Audio, Speech and Language Processing, vol.14, issue.4, pp.1462-1469, 2006.
DOI : 10.1109/TSA.2005.858005

URL : https://hal.archives-ouvertes.fr/inria-00544230

J. Durrieu, B. David, and G. Richard, A Musically Motivated Mid-Level Representation for Pitch Estimation and Musical Audio Source Separation, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.6, pp.1180-1191, 2011.
DOI : 10.1109/JSTSP.2011.2158801

P. Huang, S. D. Chen, P. Smaragdis, and M. Hasegawa-johnson, Singing-voice separation from monaural recordings using robust principal component analysis, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2012-57
DOI : 10.1109/ICASSP.2012.6287816

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

Z. Rafii and B. Pardo, REpeating Pattern Extraction Technique (REPET): A Simple Method for Music/Voice Separation, IEEE Transactions on Audio, Speech, and Language Processing, vol.21, issue.1, pp.73-84, 2013.
DOI : 10.1109/TASL.2012.2213249

A. Liutkus, Z. Rafii, R. Badeau, B. Pardo, and G. Richard, Adaptive filtering for music/voice separation exploiting the repeating musical structure, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2012-53
DOI : 10.1109/ICASSP.2012.6287815

URL : https://hal.archives-ouvertes.fr/hal-00945300

Z. Rafii and B. Pardo, Music/voice separation using the similarity matrix, Proc. ISMIR, pp.583-588

T. Development and T. , Theano: A Python framework for fast computation of mathematical expressions, " arXiv e-prints, 2016.