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In this paper, we present MusPy, an open source Python library for symbolic music generation.
R. B. Dannenberg, “A brief survey of music representation issues, techniques, and systems,” Computer Music Journal , vol. 17, no. 3, pp. 20–30, 1993
1993
Earlier work this paper cites.
M. Mozer, “Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing,” Connection Science , vol. 6, pp. 247–280, 1994
1994
Earlier work this paper cites.
W. B. Hewlett, “MuseData: Multipurpose representation,” in Beyond MIDI: The Handbook of Musical Codes , E. Selfridge-Field, Ed. Cambridge, Massachusetts: MIT Press, 1997, ch. 27, pp. 402–447
1997
Earlier work this paper cites.
D. Huron, “Humdrum and Kern: Selective feature encoding,” in Beyond MIDI: The Handbook of Musical Codes , E. Selfridge-Field, Ed. Cambridge, Massachusetts: MIT Press, 1997, ch. 27, pp. 375–401
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
M. Good, “Musicxml for notation and analysis,” in The Virtual Score: Representation, Retrieval, Restoration , W. B. Hewlett and E. Selfridge-Field, Eds. Cambridge, Massachusetts: MIT Press, 2001, ch. 8, pp. 113–124
2001
Earlier work this paper cites.
D. Eck and J. Schmidhuber, “Finding temporal structure in music: Blues improvisation with LSTM recurrent networks,” in Proc. of the IEEE Workshop on Neural Networks for Signal Processing , 2002, pp. 747–756
2002
Earlier work this paper cites.
C. Mckay and I. Fujinaga, “JSymbolic: A feature extractor for MIDI files,” in Proc. of the 2006 International Computer Music Conference (ICMC) , 2006
2006
Earlier work this paper cites.
2008
Earlier work this paper cites.
M. S. Cuthbert and C. Ariza, “Music21: A toolkit for computer-aided musicology and symbolic music data,” in Proc. of the 11th International Society for Music Information Retrieval Conference (ISMIR) , 2010
2010
Earlier work this paper cites.
S. Marcel and Y. Rodriguez, “Torchvision the machine-vision package of torch,” in Proc. of the 18th ACM International Conference on Multimedia , 2010
2010
Earlier work this paper cites.
A. Hankinson, P. Roland, and I. Fujinaga, “The music encoding initiative as a document-encoding framework,” in Proc. of the 12th International Society for Music Information Retrieval Conference (ISMIR) , 2011
2011
Earlier work this paper cites.
N. Boulanger-Lewandowski, Y. Bengio, and P. Vincent, “Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription,” in Proc. of the 29th International Conference on Machine Learning (ICML) , 2012
2012
Cited alongside, same era.
C. Raffel, B. McFee, E. J. Humphrey, J. Salamon, O. Nieto, D. Liang, and D. P. W. Ellis, “mir_eval: A transparent implementation of common MIR metrics,” in Proc. of the 15th International Society for Music Information Retrieval Conference (ISMIR) , 2014
2014
Cited alongside, same era.
C. Raffel and D. P. W. Ellis, “Intuitive analysis, creation and manipulation of MIDI data with pretty_midi,” in Late-Breaking Demos of the 15th International Society for Music Information Retrieval Conference (ISMIR) , 2014
2014
Cited alongside, same era.
K. Cho, B. van Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” in Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2014
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 Proc. of the 32nd AAAI Conference on Artificial Intelligence (AAAI) , 2018
2018
Later among the works it cites.
L.-C. Yang and A. Lerch, “On the evaluation of generative models in music,” Neural Computing and Applications , vol. 32, pp. 4773–4784, 2018
2018
Later among the works it cites.
C. Donahue, H. H. Mao, and J. McAuley, “The NES music database: A multi-instrumental dataset with expressive performance attributes,” in Proc. of the 19th International Society for Music Information Retrieval Conference (ISMIR) , 2018
2018
Later among the works it cites.
H.-W. Dong, W.-Y. Hsiao, and Y.-H. Yang, “Pypianoroll: Open source Python package for handling multitrack pianorolls,” in Late-Breaking Demos of the 19th International Society for Music Information Retrieval Conference (ISMIR) , 2018
2018
Later among the works it cites.
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2014
Cited alongside, same era.
O. Mogren, “C-RNN-GAN: Continuous recurrent neural networks with adversarial training,” in NeuIPS Worshop on Constructive Machine Learning , 2016
2016
Cited alongside, same era.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in Proc. of the 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI) , 2016
2016
Cited alongside, same era.
C. Raffel, “Learning-based methods for comparing sequences, with applications to audio-to-MIDI alignment and matching,” Ph.D. dissertation, Columbia University, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
L.-C. Yang, S.-Y. Chou, and Y.-H. Yang, “Midinet: A convolutional generative adversarial network for symbolic-domain music generation,” in Proc. of the 18th International Society for Music Information Retrieval Conference (ISMIR) , 2017
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 30 (NeurIPS) , 2017
2017
Cited alongside, same era.
A. Roberts, J. Engel, C. Raffel, C. Hawthorne, and D. Eck, “A hierarchical latent vector model for learning long-term structure in music,” in Proc. of the 35th International Conference on Machine Learning (ICML) , 2018
2018
Cited alongside, same era.
S. Oore, I. Simon, S. Dieleman, D. Eck, and K. Simonyan, “This time with feeling: Learning expressive musical performance,” Neural Computing and Applications , vol. 32, 2018
2018
Cited alongside, same era.
R. M. Bittner, M. Fuentes, D. Rubinstein, A. Jansson, K. Choi, and T. Kell, “mirdata: Software for reproducible usage of datasets,” in Proc. of the 20th International Society for Music Information Retrieval Conference (ISMIR) , 2019
2019
Later among the works it cites.
C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, I. Simon, C. Hawthorne, N. Shazeer, A. M. Dai, M. D. Hoffman, M. Dinculescu, and D. Eck, “Music transformer: Generating music with long-term structure,” in Proc. of the 7th International Conference for Learning Representations (ICLR) , 2019
2019
Later among the works it cites.
C. Donahue, H. H. Mao, Y. E. Li, G. W. Cottrell, and J. McAuley, “Lakhnes: Improving multi-instrumental music generation with cross-domain pre-training,” in Proc. of the 20th International Society for Music Information Retrieval Conference (ISMIR) , 2019
2019
Later among the works it cites.
C. Hawthorne, A. Stasyuk, A. Roberts, I. Simon, C.-Z. A. Huang, S. Dieleman, E. Elsen, J. Engel, and D. Eck, “Enabling factorized piano music modeling and generation with the MAESTRO dataset,” in Proc. of the 7th International Conference on Learning Representations (ICLR) , 2019
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “PyTorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 (NeurIPS) , 2019, pp. 8024–8035
2019
Later among the works it cites.
2020
Closest in time.
S.-L. Wu and Y.-H. Yang, “The jazz transformer on the front line: Exploring the shortcomings of ai-composed music through quantitative measures,” in Proc. of the 21st International Society for Music Information Retrieval Conference (ISMIR) , 2020
2020
Closest in time.