Fetching the paper…
Reading the bibliography…
An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes.
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.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio,” in 9th ISCA Speech Synthesis Workshop , 2016, pp. 125–125
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Engel, C. Resnick, A. Roberts, S. Dieleman, M. Norouzi, D. Eck, and K. Simonyan, “Neural audio synthesis of musical notes with wavenet autoencoders,” in International Conference on Machine Learning . PMLR, 2017, pp. 1068–1077
2017
Earlier work this paper cites.
A. van den Oord, O. Vinyals, and K. Kavukcuoglu, “Neural discrete representation learning,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17. USA: Curran Associates Inc., 2017, pp. 6309–6318. [Online]. Available: http://dl.acm.org/citation.cfm?id=3295222.3295378
2017
Earlier work this paper cites.
Y. Wang, R. J. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, and Others, “Tacotron: Towards end-to-end speech synthesis,” in Proc. Interspeech , 2017, pp. 4006–4010
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
Earlier work this paper cites.
J. Thickstun, Z. Harchaoui, and S. M. Kakade, “Learning features of music from scratch,” in International Conference on Learning Representations (ICLR) , 2017
2017
Earlier work this paper cites.
S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold, M. Slaney, R. J. Weiss, and K. Wilson, “CNN architectures for large-scale audio classification,” in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , New Orleans, USA, Mar. 2017
2017
Earlier work this paper cites.
N. Mor, L. Wolf, A. Polyak, and Y. Taigman, “A universal music translation network,” Tech. Rep., 2018. [Online]. Available: https://ytaigman.github.io/musicnet
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
N. Kalchbrenner, E. Elsen, K. Simonyan, S. Noury, N. Casagrande, E. Lockhart, F. Stimberg, A. van den Oord, S. Dieleman, and K. Kavukcuoglu, “Efficient neural audio synthesis,” in arXiv , 2018, pp. 2415–2424
2018
Earlier work this paper cites.
A. Défossez, N. Zeghidour, N. Usunier, L. Bottou, and F. Bach, “Sing: Symbol-to-instrument neural generator,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan, R. A. Saurous, Y. Agiomvrgiannakis, and Y. Wu, “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings , vol. 2018-April, 2018, pp. 4779–4783. [Online]. Available: https://google.github.io/tacotron/publications/tacotron2
2018
Earlier work this paper cites.
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville, “Film: Visual reasoning with a general conditioning layer,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
Q. Xi, R. M. Bittner, J. Pauwels, X. Ye, and J. P. Bello, “GuitarSet: A dataset for guitar transcription.” in ISMIR , 2018, pp. 453–460
2018
Earlier work this paper cites.
B. Li, X. Liu, K. Dinesh, Z. Duan, and G. Sharma, “Creating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications,” Trans. Multi. , vol. 21, no. 2, p. 522–535, feb 2019. [Online]. Available: https://doi.org/10.1109/TMM.2018.2856090
2018
Earlier work this paper cites.
N. Shazeer and M. Stern, “Adafactor: Adaptive learning rates with sublinear memory cost,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, 10–15 Jul 2018, pp. 4596–4604. [Online]. Available: https://proceedings.mlr.press/v80/shazeer18a.html
2018
Earlier work this paper cites.
K. Kumar, R. Kumar, T. de Boissiere, L. Gestin, W. Z. Teoh, J. Sotelo, A. de Brébisson, Y. Bengio, and A. C. Courville, “Melgan: Generative adversarial networks for conditional waveform synthesis,” in Advances in Neural Information Processing Systems , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019. [Online]. Available: https://proceedings.neurips.cc/paper/2019/file/6804c9bca0a615bdb9374d00a9fcba59-Paper.pdf
2019
Earlier work this paper 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 International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=r1lYRjC9F7
2019
Earlier work this paper cites.
J. W. Kim, R. Bittner, A. Kumar, and J. P. Bello, “Neural music synthesis for flexible timbre control,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 176–180
2019
Earlier work this paper cites.
L. H. Hantrakul, J. Engel, A. Roberts, and C. Gu, “Fast and flexible neural audio synthesis.” in ISMIR , 2019
2019
Earlier work this paper cites.
C. Donahue, J. McAuley, and M. Puckette, “Adversarial audio synthesis,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=ByMVTsR5KQ
2019
Cited alongside, same era.
J. Engel, K. K. Agrawal, S. Chen, I. Gulrajani, C. Donahue, and A. Roberts, “GANSynth: Adversarial neural audio synthesis,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=H1xQVn09FX
2019
Cited alongside, same era.
X. Wang and J. Yamagishi, “Neural harmonic-plus-noise waveform model with trainable maximum voice frequency for text-to-speech synthesis,” Tech. Rep., 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
R. J. Weiss, R. Skerry-Ryan, E. Battenberg, S. Mariooryad, and D. P. Kingma, “Wave-tacotron: Spectrogram-free end-to-end text-to-speech synthesis,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 5679–5683
2021
Later among the works it cites.
V. Popov, I. Vovk, V. Gogoryan, T. Sadekova, and M. Kudinov, “Grad-TTS: A diffusion probabilistic model for text-to-speech,” in International Conference on Machine Learning . PMLR, 2021, pp. 8599–8608
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Lee and S. Han, “Nu-wave: A diffusion probabilistic model for neural audio upsampling,” Proc. Interspeech 2021 , pp. 1634–1638, 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 3617–3621
2019
Cited alongside, same era.
E. Manilow, G. Wichern, P. Seetharaman, and J. Le Roux, “Cutting music source separation some Slakh: A dataset to study the impact of training data quality and quantity,” in 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) . IEEE, 2019, pp. 45–49
2019
Cited alongside, same era.
K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fréchet audio distance: A reference-free metric for evaluating music enhancement algorithms.” in INTERSPEECH , 2019, pp. 2350–2354
2019
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 6840–6851. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf
2020
Cited alongside, same era.
J. Engel, L. H. Hantrakul, C. Gu, and A. Roberts, “DDSP: Differentiable digital signal processing,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=B1x1ma4tDr
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. Engel, R. Swavely, L. H. Hantrakul, A. Roberts, and C. Hawthorne, “Self-supervised pitch detection by inverse audio synthesis,” in ICML 2020 Workshop on Self-supervision in Audio and Speech , 2020. [Online]. Available: https://openreview.net/forum?id=RlVTYWhsky7
2020
Cited alongside, same era.
R. Castellon, C. Donahue, and P. Liang, “Towards realistic midi instrument synthesizers,” in NeurIPS Workshop on Machine Learning for Creativity and Design (2020) , 2020
2020
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
D. P. Kingma, T. Salimans, B. Poole, and J. Ho, “Variational diffusion models,” in Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, Eds., 2021. [Online]. Available: https://openreview.net/forum?id=2LdBqxc1Yv
2021
Later among the works it cites.
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in Proceedings of the 38th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, M. Meila and T. Zhang, Eds., vol. 139. PMLR, 18–24 Jul 2021, pp. 8162–8171. [Online]. Available: https://proceedings.mlr.press/v139/nichol21a.html
2021
Later among the works it cites.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” in NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications , 2021. [Online]. Available: https://openreview.net/forum?id=qw8AKxfYbI
2021
Later among the works it cites.
Y. Wu, E. Manilow, Y. Deng, R. Swavely, K. Kastner, T. Cooijmans, A. Courville, C.-Z. A. Huang, and J. Engel, “Midi-ddsp: Detailed control of musical performance via hierarchical modeling,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=UseMOjWENv
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
J. P. Gardner, I. Simon, E. Manilow, C. Hawthorne, and J. Engel, “MT3: Multi-task multitrack music transcription,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=iMSjopcOn0p
2022
Closest in time.
M. Morrison, R. Kumar, K. Kumar, P. Seetharaman, A. Courville, and Y. Bengio, “Chunked autoregressive gan for conditional waveform synthesis,” in International Conference on Learning Representations (ICLR) , April 2022
2022
Closest in time.
——, “Streamable neural audio synthesis with non-causal convolutions,” 2022
2022
Closest in time.
2022
Closest in time.
N. Kandpal, O. Nieto, and Z. Jin, “Music enhancement via image translation and vocoding,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 3124–3128
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
T. Salimans and J. Ho, “Progressive distillation for fast sampling of diffusion models,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=TIdIXIpzhoI
2022
Closest in time.
“MT3: Multi-task multitrack music transcription,” https://github.com/magenta/mt3 , accessed: 2022-05-01
2022
Closest in time.