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We explore models for translating abstract musical ideas (scores, rhythms) into expressive performances using Seq2Seq and recurrent Variational Information Bottleneck (VIB) models.
Minimal time interval in auditory temporal resolution
Muchnik, C., Hildesheimer, M., Rubinstein, M., Sadeh, M., Shegter, Y., and Shibolet, B · 1985
Earlier work this paper cites.
All about quantise
Wherry, M · 2006
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Towards machine learning of expressive microtiming in brazilian drumming
Wright, M. and Berdahl, E · 2006
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Imitating the groove: Making drum machines more human
Tidemann, A. and Demiris, Y · 2007
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Groovy neural networks
Tidemann, A. and Demiris, Y · 2008
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A groovy virtual drumming agent
Tidemann, A., Öztürk, P., and Demiris, Y · 2009
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Modeling piano interpretation using switching kalman filter
Gu, Y. and Raphael, C · 2012
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Creating expressive piano performance using a low-dimensional performance model
Gu, Y. and Raphael, C · 2013
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The effects of unilateral tinnitus on auditory temporal resolution: gaps-in-noise performance
An, Y.-H., Jin, S. Y., Yoon, S. W., and Shim, H. J · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
Earlier work this paper cites.
Quantifying microtiming patterning and variability in drum kit recordings: A method and some data
Hellmer, K. and Madison, G · 2015
Earlier work this paper cites.
Tensorflow: a system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
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Temporal resolution and active auditory discrimination skill in vocal musicians
Kumar, P., Sanju, H. K., and Nikhil, J · 2016
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Attribute2image: Conditional image generation from visual attributes
Yan, X., Yang, J., Sohn, K., and Lee, H · 2016
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
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Enabling factorized piano music modeling and generation with the maestro dataset
Hawthorne, C., Stasyuk, A., Roberts, A., Simon, I., Huang, C.-Z. A., Dieleman, S., Elsen, E., Engel, J., and Eck, D · 2018
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Huang, C.-Z. A., Vaswani, A., Uszkoreit, J., Simon, I., Hawthorne, C., Shazeer, N., Dai, A. M., Hoffman, M. D., Dinculescu, M., and Eck, D · 2018
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Infilling piano performances
Ippolito, D., Huang, A., Hawthorne, C., and Eck, D · 2018
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This time with feeling: Learning expressive musical performance
Oore, S., Simon, I., Dieleman, S., Eck, D., and Simonyan, K · 2018
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A hierarchical latent vector model for learning long-term structure in music
Roberts, A., Engel, J., Raffel, C., Hawthorne, C., and Eck, D · 2018
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Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., and Hinton, G · 2017
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Salimans, T., Karpathy, A., Chen, X., and Kingma, D. P · 2017
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Performance rnn: Generating music with expressive timing and dynamics, 2017
Simon, I. and Oore, S · 2017
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Thermometer encoding: One hot way to resist adversarial examples
Buckman, J., Roy, A., Raffel, C., and Goodfellow, I · 2018
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Semi-supervised training for improving data efficiency in end-to-end speech synthesis
Chung, Y.-A., Wang, Y., Hsu, W.-N., Zhang, Y., and Skerry-Ryan, R · 2018
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Hierarchical neural story generation
Fan, A., Lewis, M., and Dauphin, Y · 2018
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Wang, T.-C., Liu, M.-Y., Zhu, J.-Y., Liu, G., Tao, A., Kautz, J., and Catanzaro, B
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Groove in drum patterns as a function of both rhythmic properties and listeners’ attitudes
Senn, O., Kilchenmann, L., Bechtold, T., and Hoesl, F · 2018
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Natural tts synthesis by conditioning wavenet on mel spectrogram predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al · 2018
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Learning a latent space of multitrack measures
Simon, I., Roberts, A., Raffel, C., Engel, J., Hawthorne, C., and Eck, D · 2018
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Predicting expressive speaking style from text in end-to-end speech synthesis
Stanton, D., Wang, Y., and Skerry-Ryan, R · 2018
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Jazzgan: Improvising with generative adversarial networks
Trieu, N. and Keller, R. M · 2018
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Huang, C.-Z. A., Cooijmans, T., Roberts, A., Courville, A., and Eck, D · 2019
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