C. Koechlin, Traité de l'orchestration. ´ Editions Max Eschig, 1941.

S. Mcadams, Timbre as a structuring force in music, Proceedings of Meetings on Acoustics, p.35050, 2013.

D. Tardieu and S. Mcadams, Perception of Dyads of Impulsive and Sustained Instrument Sounds, Music Perception: An Interdisciplinary Journal, vol.30, issue.2, pp.117-128, 2012.
DOI : 10.1525/mp.2012.30.2.117
URL : https://hal.archives-ouvertes.fr/hal-01106662

P. Esling, Multiobjective time series matching and classification, Ph.D. dissertation, IRCAM, 2012.
DOI : 10.1109/tasl.2013.2265086

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.35, issue.8, pp.1798-1828, 2013.
DOI : 10.1109/tpami.2013.50
URL : http://arxiv.org/abs/1206.5538

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444
DOI : 10.1007/s10994-013-5335-x

E. J. Humphrey, J. P. Bello, and Y. Lecun, Moving beyond feature design: Deep architectures and automatic feature learning in music informatics, ISMIR. Citeseer, pp.403-408, 2012.
DOI : 10.1007/s10844-013-0248-5

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, Unsupervised learning of hierarchical representations with convolutional deep belief networks, Communications of the ACM, vol.54, issue.10, pp.95-103, 2011.
DOI : 10.1145/2001269.2001295

N. Boulanger-lewandowski, Y. Bengio, and P. Vincent, Audio chord recognition with recurrent neural networks, ISMIR. Citeseer, pp.335-340, 2013.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed et al., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine, vol.29, issue.6, pp.82-97, 2012.
DOI : 10.1109/MSP.2012.2205597

A. Van-den-oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals et al., Wavenet: A generative model for raw audio, 1609.

D. Eck and J. Schmidhuber, Finding temporal structure in music: blues improvisation with LSTM recurrent networks, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, pp.747-756, 2002.
DOI : 10.1109/NNSP.2002.1030094

V. Lavrenko and J. Pickens, Polyphonic music modeling with random fields, Proceedings of the eleventh ACM international conference on Multimedia , MULTIMEDIA '03, pp.120-129, 2003.
DOI : 10.1145/957013.957041
URL : http://ciir.cs.umass.edu/pubfiles/mm-45.pdf

S. Bosley, P. Swire, and R. M. Keller, Learning to create jazz melodies using deep belief nets, 2010.

N. Boulanger-lewandowski, Y. Bengio, and P. Vincent, Modeling temporal dependencies in highdimensional sequences: Application to polyphonic music generation and transcription, 2012.

F. Sun, Deephear -composing and harmonizing music with neural networks

G. Carpentier, D. Tardieu, J. Harvey, G. Assayag, and E. Saint-james, Predicting Timbre Features of Instrument Sound Combinations: Application to Automatic Orchestration, Journal of New Music Research, vol.45, issue.1, pp.47-61, 2010.
DOI : 10.1162/comj.2009.33.1.32

P. Esling, G. Carpentier, and C. Agon, Dynamic Musical Orchestration Using Genetic Algorithms and a Spectro-Temporal Description of Musical Instruments, Applications of Evolutionary Computation, pp.371-380, 2010.
DOI : 10.1007/978-3-642-12242-2_38

F. Pachet, A Joyful Ode to Automatic Orchestration, ACM Transactions on Intelligent Systems and Technology, vol.8, issue.2, p.18, 2016.
DOI : 10.1109/2.84836

E. Handelman, A. Sigler, and D. Donna, Automatic orchestration for automatic composition, 1st International Workshop on Musical Metacreation, pp.43-48, 2012.

G. W. Taylor, G. E. Hinton, and S. T. Roweis, Modeling human motion using binary latent variables, Advances in neural information processing systems, pp.1345-1352, 2006.

K. Yao, T. Cohn, K. Vylomova, K. Duh, and C. Dyer, Depth-gated LSTM

A. Fischer and C. Igel, Progress in Pattern Recognition , Image Analysis, Computer Vision, and Applications: 17th Iberoamerican Congress, CIARP 2012, Buenos Aires, Argentina, Proceedings. Berlin, pp.14-36, 2012.

G. Hinton, A Practical Guide to Training Restricted Boltzmann Machines, Momentum, vol.79, issue.7, p.926, 2010.
DOI : 10.1145/1390156.1390290
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.170.9573

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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.124.730

G. W. Taylor and G. E. Hinton, Factored conditional restricted Boltzmann Machines for modeling motion style, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1025-1032, 2009.
DOI : 10.1145/1553374.1553505
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.8095

R. Kiros, Y. Zhu, R. R. Salakhutdinov, R. Zemel, R. Urtasun et al., Skip-thought vectors, Advances in Neural Information Processing Systems, pp.3276-3284, 2015.