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Generative AI models have recently blossomed, significantly impacting artistic and musical traditions.
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L.-C. Yang, S.-Y. Chou, and Y.-H. Yang, “MIDINet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation,” in International Society for Music Information Retrieval Conference , 2017, pp. 324–331
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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 Conf. on Artificial Intelligence (AAAI) , 2018
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2019
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C.-Z. A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, I. Simon, C. Hawthorne, A. M. Dai, M. D. Hoffman, M. Dinculescu, and D. Eck, “Music Transformer: Generating Music with Long-term Structure,” in Proc. of the 7th Int. Conf. on Learning Representations , New Orleans, LA, USA, 2019
2019
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P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever, “Jukebox: A Generative Model for Music,” 2020. [Online]. Available: https://github.com/openai/jukebox
2020
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2022
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K. Déguernel, H. Maruri-Aguilar, and B. L. T. Sturm, “Investigating the Relationship Between Liking and Belief in AI Authorship in the Context of Irish Traditional Music,” in CREAI 2022 Workshop on Artificial Intelligence and Creativity , 2022
2022
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J. Loth, P. Sarmento, C. Carr, Z. Zukowski, and M. Barthet, “ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production,” in The 16th International Symposium on Computer Music Multidisciplinary Research , 2023
2023
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2023
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H. H. Tan and D. Herremans, “Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling,” in Proc. of the 21st Int. Soc. for Music Information Retrieval Conf. , Montréal, Canada, 2020, pp. 109–116
2020
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Y.-S. Huang and Y.-H. Yang, “Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions,” in Proc. of the 28th ACM Int. Conf. on Multimedia , Seattle, WA, USA, 2020, pp. 1180–1188
2020
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L.-C. Yang and A. Lerch, “On the Evaluation of Generative Models in Music,” Neural Computing and Applications , vol. 32, 05 2020
2020
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G. Born, “Diversifying MIR: Knowledge and Real-World Challenges, and New Interdisciplinary Futures,” Transactions of the International Society for Music Information Retrieval , vol. 3, no. 1, pp. 193–204, 2020
2020
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2020
Cited alongside, same era.
P. Sarmento, “Perspectives on the Future for Sonic Writers,” Journal of Science and Technology of the Arts , vol. 13, no. 1, pp. 110–114, 2021
2021
Cited alongside, same era.
P. Sarmento, A. Kumar, C. Carr, Z. Zukowski, M. Barthet, and Y.-H. Yang, “DadaGP: a Dataset of Tokenized GuitarPro Songs for Sequence Models,” in Proc. of the 22nd Int. Soc. for Music Information Retrieval Conf. , 2021, pp. 610–618
2021
Cited alongside, same era.
C. J. Steinmetz and J. D. Reiss, “pyloudnorm: A Simple yet Flexible Loudness Meter in Python,” in 150th AES Convention , 2021
2021
Cited alongside, same era.
J. Copet, F. Kreuk, I. Gat, T. Remez, D. Kant, G. Synnaeve, Y. Adi, and A. Défossez, “Simple and Controllable Music Generation,” in 37th Conference on Neural Information Processing Systems (NeurIPS) , 2023
2023
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J. Barnett, “The ethical implications of generative audio models: A systematic literature review,” in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES ’23) , 2023, p. 10, 1 figure
2023
Later among the works it cites.
P. Sarmento, A. Kumar, Y.-H. Chen, C. Carr, Z. Zukowski, and M. Barthet, “GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers,” in Proceedings of the EvoMUSART Conference , 2023
2023
Later among the works it cites.
S. Adkins, P. Sarmento, and M. Barthet, “LooperGP: A Loopable Sequence Model for Live Coding Performance using GuitarPro Tablature,” in Proceedings of the EvoMUSART Conference , 2023
2023
Later among the works it cites.
P. Sarmento, A. Kumar, D. Xie, C. Carr, Z. Zukowski, and M. Barthet, “ShredGP: Guitarist Style-Conditioned Tablature Generation,” in The 16th International Symposium on Computer Music Multidisciplinary Research , Tokyo, Japan, 2023
2023
Later among the works it cites.
S. Yang, C. N. Reed, E. Chew, and M. Barthet, “Examining Emotion Perception Agreement in Live Music Performance,” IEEE Transactions on Affective Computing , vol. 14, no. 2, pp. 1442–1460, 2023
2023
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Z. Evans, C. Carr, J. Taylor, S. H. Hawley, and J. Pons, “Fast Timing-Conditioned Latent Audio Diffusion,” in Proceedings of the 41st International Conference on Machine Learning , 2024
2024
Closest in time.
B. L. T. Sturm, M. Iglesias, O. Ben-Tal, M. Miron, and E. Gómez, “Artificial Intelligence and Music: Open Questions of Copyright Law and Engineering Praxis,” Arts , vol. 8, no. 3, 2019. [Online]. Available: https://www.mdpi.com/2076-0752/8/3/115
2076
Closest in time.