M. Bay, F. Andreas, J. Ehmann, and . Stephen-downie, Evaluation of multiple-f0 estimation and tracking systems, In ISMIR, pp.315-320, 2009.

M. Christopher and . Bishop, Pattern recognition and machine learning, 2006.

N. Boulanger-lewandowski, Y. Bengio, and P. Vincent, Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. arXiv preprint arXiv:1206, 2012.

D. Conklin, H. Ian, and . Witten, Multiple viewpoint systems for music prediction, Journal of New Music Research, vol.4, issue.1, pp.51-73, 1995.
DOI : 10.2307/3680523
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.7482

J. Cookerly, Complete orchestration system, US Patent, vol.7, p.718883, 2010.

L. Crestel and P. Esling, Live orchestral piano, a system for real-time orchestral music generation, Proceedings of the 14th Sound and Music Computing Conference, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01577463

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.

M. Grachten, M. Gasser, A. Arzt, and G. Widmer, Automatic alignment of music performances with structural differences, Proceedings of 14th International Society for Music Information Retrieval Conference, 2013.

J. Huang, S. Chiu, and M. Shan, Towards an automatic music arrangement framework using score reduction, ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), p.8, 2012.
DOI : 10.1145/2071396.2071404

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

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

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

S. Sven-amin-lembke and . Mcadams, Timbre blending of wind instruments: acoustics and perception, 2012.

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

S. Mcadams, L. Bruno, and . Giordano, The perception of musical timbre. The Oxford handbook of music psychology, pp.72-80, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01105517

M. Miron, J. Janer, and E. Gómez, Generating data to train convolutional neural networks for classical music source separation, Proceedings of the 14th Sound and Music Computing Conference, pp.227-233, 2017.

B. Saul, C. D. Needleman, and . Wunsch, A general method applicable to the search for similarities in the amino acid sequence of two proteins, Journal of Molecular Biology, vol.48, issue.3, pp.443-453, 1970.

F. Pachet, A Joyful Ode to Automatic Orchestration, ACM Transactions on Intelligent Systems and Technology, vol.8, issue.2, pp.1-1813, 2016.
DOI : 10.1109/2.84836

G. Peeters, L. Bruno, P. Giordano, N. Susini, S. Misdariis et al., The Timbre Toolbox: Extracting audio descriptors from musical signals, The Journal of the Acoustical Society of America, vol.130, issue.5, pp.2902-2916, 2011.
DOI : 10.1121/1.3642604

D. Pressnitzer, S. Mcadams, S. Winsberg, and J. Fineberg, Perception of musical tension for nontonal orchestral timbres and its relation to psychoacoustic roughness, Perception & Psychophysics, vol.29, issue.1, pp.66-80, 2000.
DOI : 10.1121/1.1908963
URL : https://hal.archives-ouvertes.fr/hal-01105566

H. Takamori, H. Sato, T. Nakatsuka, and S. Morishima, Automatic arranging musical score for piano using important musical elements, Proceedings of the 14th Sound and Music Computing Conference, 2017.

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

W. Graham, . Taylor, E. Geoffrey, and . Hinton, Factored conditional restricted boltzmann machines for modeling motion style, Proceedings of the 26th annual international conference on machine learning, pp.1025-1032, 2009.