A. Ahn, Generative adversarial network, 2017.

J. S. Bach, 389 Chorales (Choral-Gesange), 1985.

J. Briot, Gaëtan Hadjeres, and François Pachet. Deep learning techniques for music generation ? A survey, 2017.

N. Boulanger-lewandowski, Chapter 14th ? Modeling and generating sequences of polyphonic music with the RNN-RBM, pp.149-158, 2015.

[. Boulanger-lewandowski, Y. Bengio, and P. Vincent, Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription, Proceedings of the 29th International Conference on Machine Learning (ICML-12), pp.1159-1166, 2012.

M. Bretan, G. Weinberg, and L. Heck, A unit selection methodology for music generation using deep neural networks, 2016.

D. Castelvecchi, Can we open the black box of AI?, Nature, vol.538, issue.7623, pp.20-23, 2016.
DOI : 10.1038/538020a

Y. Le-cun and Y. Bengio, Convolutional networks for images, speech, and time-series In The handbook of brain theory and neural networks, pp.255-258, 1998.

J. Deltorn, Deep Creations: Intellectual Property and the Automata, Frontiers in Digital Humanities, vol.13, 2017.
DOI : 10.1007/s13347-016-0243-1

K. Doya and E. Uchibe, The Cyber Rodent project: Exploration of adaptive mechanisms for selfpreservation and self-reproduction, Adaptive Behavior, issue.2, pp.149-160, 2005.

K. Ebcioglu, An Expert System for Harmonizing Four-Part Chorales, Computer Music Journal, vol.12, issue.3, pp.43-51, 1988.
DOI : 10.2307/3680335

D. Erhan, Y. Bengio, A. Courville, P. Manzagol, and P. Vincent, Why does unsupervised pre-training help deep learning, Journal of Machine Learning Research, issue.11, pp.625-660, 2010.

A. Elgammal, B. Liu, M. Elhoseiny, and M. Mazzone, CAN: Creative adversarial networks generating " art " by learning about styles and deviating from style norms, 2017.

D. Eck and J. Schmidhuber, A first look at music composition using LSTM recurrent neural networks, 2002.

R. Fiebrink and B. Caramiaux, The machine learning algorithm as creative musical tool, 2016.

[. Foote, D. Yang, and M. Rohaninejad, Audio style transfer ? Do androids dream of electric beats?, 2016.

[. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

L. A. Gatys, A. S. Ecker, and M. Bethge, A Neural Algorithm of Artistic Style, Journal of Vision, vol.16, issue.12, 2015.
DOI : 10.1167/16.12.326

A. Graves, Generating sequences with recurrent neural networks, 2014.

G. Sylvain-le and P. F. Verschure, Adaptive music generation by reinforcement learning of musical tension, Proceedings of the 7th Sound and Music Computing Conference (SMC'2010), 2010.

[. Herremans, C. Chuan, and E. Chew, A Functional Taxonomy of Music Generation Systems, ACM Computing Surveys, vol.50, issue.5, 2017.
DOI : 10.1109/CVPR.1992.223161

G. E. Hinton, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol.22, issue.8, pp.1771-1800, 2002.
DOI : 10.1162/089976600300015385

G. E. Hinton, S. Osindero, and Y. Teh, A Fast Learning Algorithm for Deep Belief Nets, Neural Computation, vol.18, issue.7, pp.1527-1554, 2006.
DOI : 10.1162/jmlr.2003.4.7-8.1235

G. Hadjeres, F. Pachet, and F. Nielsen, DeepBach: a steerable model for Bach chorales generation, 2017.

E. Geoffrey, T. J. Hinton, and . Sejnowski, Learning and relearning in Boltzmann machines, Parallel Distributed Processing ? Explorations in the Microstructure of Cognition: Volume 1 Foundations, pp.282-317, 1986.

[. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

E. Geoffrey, R. R. Hinton, and . Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, issue.5786, pp.313504-507, 2006.

[. Ismir, International Society for Music Information Retrieval Conference(s) (Proceedings), Accessed on 23, 2017.

[. Jaques, S. Gu, R. E. Turner, and D. Eck, Tuning recurrent neural networks with reinforcement learning, 2016.

L. Pack-kaelbling, M. L. Littman, and A. W. Moore, Reinforcement learning: A survey, Journal of Artificial Intelligence Research, issue.4, pp.237-285, 1996.

P. Lam, MCMC methods: Gibbs sampling and the Metropolis-Hastings algorithm, Accessed on 21, 2016.

S. Lattner, M. Grachten, and G. Widmer, Imposing higher-level structure in polyphonic music generation using convolutional restricted Boltzmann machines and constraints, 2016.

F. Liang and . Bachbot, Automatic composition in the style of Bach chorales ? Developing, analyzing , and evaluating a deep LSTM model for musical style, Machine Learning, Speech, and Language Technology, 2016.

V. Quoc, . Le, R. Marc-'aurelio-ranzato, M. Monga, K. Devin et al., Building high-level features using large scale unsupervised learning, 29th International Conference on Machine Learning, 2012.

B. Mcfee and J. P. Bello, Structured training for large-vocabulary chord recognition, Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR'2017). ISMIR, 2017.

[. Makris, M. Kaliakatsos-papakostas, I. Karydis, and K. L. Kermanidis, Combining LSTM and Feed Forward Neural Networks for Conditional Rhythm Composition, Engineering Applications of Neural Networks: 18th International Conference Proceedings, pp.570-582, 2017.
DOI : 10.1162/014892601300126106

O. Mogren and .. , Continuous recurrent neural networks with adversarial training, 2016.

[. Mordvintsev, C. Olah, and M. Tyka, Deep Dream, 2015.

D. Morris, I. Simon, and S. Basu, Exposing parameters of a trained dynamic model for interactive music creation, Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI'2008), pp.784-791, 2008.

G. Nierhaus, Algorithmic Composition: Paradigms of Automated Music Generation, 2009.
DOI : 10.1007/978-3-211-75540-2

F. Pachet, A. Papadopoulos, and P. Roy, Sampling variations of sequences for structured music generation, Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR'2017), pp.167-173, 2017.

[. Pachet and P. Roy, Markov constraints: steerable generation of Markov sequences, Constraints, vol.37, issue.4, pp.148-172, 2011.
DOI : 10.1109/18.87000

A. Papadopoulos, P. Roy, and F. Pachet, Avoiding plagiarism in Markov sequence generation, Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp.2731-2737, 2014.

A. Papadopoulos, P. Roy, and F. Pachet, Assisted lead sheet composition using Flow- Composer, Principles and Practice of Constraint Programming: 22nd International Conference Proceedings, pp.769-785, 2016.
DOI : 10.1007/978-3-319-44953-1_48

G. Papadopoulos and G. Wiggins, Ai methods for algorithmic composition: A survey, a critical view and future prospects, AISB'1999 Symposium on Musical Creativity, pp.110-117, 1999.

D. Ringach and R. Shapley, Reverse correlation in neurophysiology, Cognitive Science, vol.26, issue.1, pp.147-166, 2004.
DOI : 10.1109/TBME.1979.326401

M. Andy, M. Sarroff, and . Casey, Musical audio synthesis using autoencoding neural nets, 2014.

B. L. Sturm, J. F. Santos, O. Ben-tal, and I. Korshunova, Music transcription modelling and composition using deep learning, 2016.

[. Steedman, A Generative Grammar for Jazz Chord Sequences, Music Perception: An Interdisciplinary Journal, vol.2, issue.1, pp.52-77, 1984.
DOI : 10.2307/40285282

F. Sun, DeepHear ? Composing and harmonizing music with neural networks, Accessed on 01, 2017.

C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan et al., Intriguing properties of neural networks, 2014.

D. Temperley, The Cognition of Basic Musical Structures [UL16] Dmitry Ulyanov and Vadim Lebedev. Audio texture synthesis and style transfer, 2011.

S. Dieleman, H. Zen, and K. Simonyan, WaveNet: A generative model for raw audio, 2016.

A. Hado-van-hasselt, D. Guez, and . Silver, Deep reinforcement learning with double Q-learning, 2015.

L. Yang, S. Chou, and Y. Yang, MidiNet: A convolutional generative adversarial network for symbolic-domain music generation, Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR'2017), 2017.

[. Yan, J. Yang, K. Sohn, and H. Lee, Attribute2Image: Conditional Image Generation from Visual Attributes, 2016.
DOI : 10.1109/TPAMI.2011.208