Fetching the paper…
Reading the bibliography…
While deep generative models have become the leading methods for algorithmic composition, it remains a challenging problem to control the generation process because the latent variables of most deep-learning models lack good interpretability.
B. Pardo and W. P. Birmingham, “Algorithms for chordal analysis,” Computer Music Journal , vol. 26, no. 2, pp. 27–49, 2002
2002
Earlier work this paper cites.
P.-T. De Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, “A tutorial on the cross-entropy method,” Annals of operations research , vol. 134, no. 1, pp. 19–67, 2005
2005
Earlier work this paper cites.
H. Hsu and P. A. Lachenbruch, “Paired t test,” Encyclopedia of Biostatistics , vol. 6, 2005
2005
Earlier work this paper cites.
I. Simon, D. Morris, and S. Basu, “Mysong: automatic accompaniment generation for vocal melodies,” in Proceedings of the SIGCHI conference on human factors in computing systems , 2008, pp. 725–734
2008
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
C. Raffel, B. McFee, E. J. Humphrey, J. Salamon, O. Nieto, D. Liang, D. P. Ellis, and C. C. Raffel, “mir_eval: A transparent implementation of common mir metrics,” in In Proceedings of the 15th International Society for Music Information Retrieval Conference, ISMIR . Citeseer, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. F. Mathieu, J. J. Zhao, J. Zhao, A. Ramesh, P. Sprechmann, and Y. LeCun, “Disentangling factors of variation in deep representation using adversarial training,” in Advances in neural information processing systems , 2016, pp. 5040–5048
2016
Earlier work this paper cites.
X. Yan, J. Yang, K. Sohn, and H. Lee, “Attribute2image: Conditional image generation from visual attributes,” in European Conference on Computer Vision . Springer, 2016, pp. 776–791
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
G. Hadjeres, F. Pachet, and F. Nielsen, “Deepbach: a steerable model for bach chorales generation,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 1362–1371
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
2018
Cited alongside, same era.
H. Zhu, Q. Liu, N. J. Yuan, C. Qin, J. Li, K. Zhang, G. Zhou, F. Wei, Y. Xu, and E. Chen, “Xiaoice band: A melody and arrangement generation framework for pop music,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2018, pp. 2837–2846
2018
2019
Later among the works it cites.
O. Kwon, I. Jang, C. Ahn, and H.-G. Kang, “Emotional speech synthesis based on style embedded tacotron2 framework,” in 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) . IEEE, 2019, pp. 1–4
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Akama, “Controlling symbolic music generation based on concept learning from domain knowledge.” in ISMIR , 2019, pp. 816–823
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
H.-W. Dong, W.-Y. Hsiao, L.-C. Yang, and Y.-H. Yang, “Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
H. Kim and A. Mnih, “Disentangling by factorising,” arXiv preprint arXiv:1802.05983 , 2018
2018
Cited alongside, same era.
K. Chen, W. Zhang, S. Dubnov, G. Xia, and W. Li, “The effect of explicit structure encoding of deep neural networks for symbolic music generation,” in 2019 International Workshop on Multilayer Music Representation and Processing (MMRP) . IEEE, 2019, pp. 77–84
2019
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2020
Closest in time.
J.-P. Briot and F. Pachet, “Deep learning for music generation: challenges and directions,” Neural Computing and Applications , vol. 32, no. 4, pp. 981–993, 2020
2020
Closest in time.
Z. Wang, Y. Zhang, Y. Zhang, J. Jiang, R. Yang, J. Zhao, and G. Xia, “Pianotree vae: Structured representation learning for polyphonic music,” in Proceedings of 21st International Conference on Music Information Retrieval (ISMIR), virtual conference , 2020
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
Z. Wang, K. Chen, J. Jiang, Y. Zhang, M. Xu, S. Dai, X. Gu, and G. Xia, “Pop909: A pop-song dataset for music arrangement generation,” in Proceedings of 21st International Conference on Music Information Retrieval (ISMIR), virtual conference , 2020
2020
Closest in time.