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K. Choi, C. Hawthorne, I. Simon, M. Dinculescu, and J. Engel, “Encoding musical style with transformer autoencoders,” in International Conference on Machine Learning . PMLR, 2020, pp. 1899–1908
1908
Earlier work this paper cites.
P. R. Cook, Music, Cognition, and Computerized Sound: An Introduction to Psychoacoustics . MIT press, 2001
2001
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
H. H. Mao, T. Shin, and G. Cottrell, “Deepj: Style-specific music generation,” in 2018 IEEE 12th International Conference on Semantic Computing (ICSC) . IEEE, 2018, pp. 377–382
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.
N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong, and R. Socher, “Ctrl: A conditional transformer language model for controllable generation,” arXiv preprint arXiv:1909.05858 , 2019
Original
2019
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT (1) . Association for Computational Linguistics, 2019, pp. 4171–4186
2019
Earlier work this paper cites.
Y. Zhang, Z. Wang, D. Wang, and G. Xia, “BUTTER: A representation learning framework for bi-directional music-sentence retrieval and generation,” in Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA) . Association for Computational Linguistics, 16 Oct. 2020, pp. 54–58
2020
Earlier work this paper cites.
Z. Wang, D. Wang, Y. Zhang, and G. Xia, “Learning interpretable representation for controllable polyphonic music generation,” arXiv preprint arXiv:2008.07122 , 2020
Original
2020
Earlier work this paper cites.
H. H. Tan and D. Herremans, “Music fadernets: Controllable music generation based on high-level features via low-level feature modelling,” arXiv preprint arXiv:2007.15474 , 2020
Original
2020
Earlier work this paper cites.
J. Ens and P. Pasquier, “Mmm: Exploring conditional multi-track music generation with the transformer,” arXiv preprint arXiv:2008.06048 , 2020
Original
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
Z. Wang, K. Chen, J. Jiang, Y. Zhang, M. Xu, S. Dai, and G. Xia, “POP909: A pop-song dataset for music arrangement generation,” in Proceedings of International Society for Music Information Retrieval Conference (ISMIR) , 2020, pp. 38–45
2020
Earlier work this paper cites.
Y.-S. Huang and Y.-H. Yang, “Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions,” in Proceedings of ACM International Conference on Multimedia (MM) , 2020, pp. 1180–1188
2020
Earlier work this paper cites.
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret, “Transformers are rnns: Fast autoregressive transformers with linear attention,” in International Conference on Machine Learning . PMLR, 2020, pp. 5156–5165
2020
Earlier work this paper cites.

Similar

H.-T. Hung, J. Ching, S. Doh, N. Kim, J. Nam, and Y.-H. Yang, “Emopia: A multi-modal pop piano dataset for emotion recognition and emotion-based music generation,” arXiv preprint arXiv:2108.01374 , 2021
Original
2021
Cited alongside, same era.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Cited alongside, same era.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” arXiv preprint arXiv:2101.00190 , 2021
Original
2021
Cited alongside, same era.
M. Zeng, X. Tan, R. Wang, Z. Ju, T. Qin, and T.-Y. Liu, “MusicBERT: Symbolic music understanding with large-scale pre-training,” in Findings of the Association for Computational Linguistics (ACL Findings) , 2021, pp. 791–800
2021
Cited alongside, same era.

Then

X. Zhang, J. Zhang, Y. Qiu, L. Wang, and J. Zhou, “Structure-enhanced pop music generation via harmony-aware learning,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 1204–1213
2022
Later among the works it cites.
S. Wu, X. Li, and M. Sun, “Chord-conditioned melody choralization with controllable harmonicity and polyphonicity,” arXiv preprint arXiv:2202.08423 , 2022
Original
2022
Later among the works it cites.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125 , 2022
Original
2022
Later among the works it cites.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems , vol. 35, pp. 36 479–36 494, 2022
2022

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2023

MuseCoco: Generating Symbolic Music from Text

Lu, Peiling, Xu, Xin, Kang, Chenfei et al.

Understand

Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement.

While some approaches utilize texts to control music audio generation, editing musical elements in generated audio is challenging for users.
In contrast, symbolic music offers ease of editing, making it more accessible for users to manipulate specific musical elements.
In this paper, we propose MuseCoco, which generates symbolic music from text descriptions with musical attributes as the bridge to break down the task into text-to-attribute understanding and attribute-to-music generation stages.

Built on

K. Choi, C. Hawthorne, I. Simon, M. Dinculescu, and J. Engel, “Encoding musical style with transformer autoencoders,” in International Conference on Machine Learning . PMLR, 2020, pp. 1899–1908
1908
Earlier work this paper cites.
P. R. Cook, Music, Cognition, and Computerized Sound: An Introduction to Psychoacoustics . MIT press, 2001
2001
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
H. H. Mao, T. Shin, and G. Cottrell, “Deepj: Style-specific music generation,” in 2018 IEEE 12th International Conference on Semantic Computing (ICSC) . IEEE, 2018, pp. 377–382
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.
N. S. Keskar, B. McCann, L. R. Varshney, C. Xiong, and R. Socher, “Ctrl: A conditional transformer language model for controllable generation,” arXiv preprint arXiv:1909.05858 , 2019
Original
2019
Earlier work this paper cites.
J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: pre-training of deep bidirectional transformers for language understanding,” in NAACL-HLT (1) . Association for Computational Linguistics, 2019, pp. 4171–4186
2019
Earlier work this paper cites.
Y. Zhang, Z. Wang, D. Wang, and G. Xia, “BUTTER: A representation learning framework for bi-directional music-sentence retrieval and generation,” in Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA) . Association for Computational Linguistics, 16 Oct. 2020, pp. 54–58
2020
Earlier work this paper cites.
Z. Wang, D. Wang, Y. Zhang, and G. Xia, “Learning interpretable representation for controllable polyphonic music generation,” arXiv preprint arXiv:2008.07122 , 2020
Original
2020
Earlier work this paper cites.
H. H. Tan and D. Herremans, “Music fadernets: Controllable music generation based on high-level features via low-level feature modelling,” arXiv preprint arXiv:2007.15474 , 2020
Original
2020
Earlier work this paper cites.
J. Ens and P. Pasquier, “Mmm: Exploring conditional multi-track music generation with the transformer,” arXiv preprint arXiv:2008.06048 , 2020
Original
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
Z. Wang, K. Chen, J. Jiang, Y. Zhang, M. Xu, S. Dai, and G. Xia, “POP909: A pop-song dataset for music arrangement generation,” in Proceedings of International Society for Music Information Retrieval Conference (ISMIR) , 2020, pp. 38–45
2020
Earlier work this paper cites.
Y.-S. Huang and Y.-H. Yang, “Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions,” in Proceedings of ACM International Conference on Multimedia (MM) , 2020, pp. 1180–1188
2020
Earlier work this paper cites.
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret, “Transformers are rnns: Fast autoregressive transformers with linear attention,” in International Conference on Machine Learning . PMLR, 2020, pp. 5156–5165
2020
Earlier work this paper cites.

Similar

H.-T. Hung, J. Ching, S. Doh, N. Kim, J. Nam, and Y.-H. Yang, “Emopia: A multi-modal pop piano dataset for emotion recognition and emotion-based music generation,” arXiv preprint arXiv:2108.01374 , 2021
Original
2021
Cited alongside, same era.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Cited alongside, same era.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” arXiv preprint arXiv:2101.00190 , 2021
Original
2021
Cited alongside, same era.
M. Zeng, X. Tan, R. Wang, Z. Ju, T. Qin, and T.-Y. Liu, “MusicBERT: Symbolic music understanding with large-scale pre-training,” in Findings of the Association for Computational Linguistics (ACL Findings) , 2021, pp. 791–800
2021
Cited alongside, same era.

Then

X. Zhang, J. Zhang, Y. Qiu, L. Wang, and J. Zhou, “Structure-enhanced pop music generation via harmony-aware learning,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 1204–1213
2022
Later among the works it cites.
S. Wu, X. Li, and M. Sun, “Chord-conditioned melody choralization with controllable harmonicity and polyphonicity,” arXiv preprint arXiv:2202.08423 , 2022
Original
2022
Later among the works it cites.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125 , 2022
Original
2022
Later among the works it cites.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems , vol. 35, pp. 36 479–36 494, 2022
2022

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

J. Ens and P. Pasquier, “Building the metamidi dataset: Linking symbolic and audio musical data.” in ISMIR , 2021, pp. 182–188

2021

Cited alongside, same era.

M. Chen, X. Tan, B. Li, Y. Liu, T. Qin, S. Zhao, and T.-Y. Liu, “Adaspeech: Adaptive text to speech for custom voice,” arXiv preprint arXiv:2103.00993 , 2021

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2021

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S. Wu and M. Sun, “Exploring the efficacy of pre-trained checkpoints in text-to-music generation task,” arXiv preprint arXiv:2211.11216 , 2022

Original

2022

Cited alongside, same era.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR . IEEE, 2022, pp. 10 674–10 685

2022

Cited alongside, same era.

Q. Huang, A. Jansen, J. Lee, R. Ganti, J. Y. Li, and D. P. W. Ellis, “Mulan: A joint embedding of music audio and natural language,” arXiv preprint arXiv:2208.12415 , 2022

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2022

Cited alongside, same era.

P. Neves, J. Fornari, and J. Florindo, “Generating music with sentiment using transformer-gans,” arXiv preprint arXiv:2212.11134 , 2022

Original

2022

Cited alongside, same era.

Y. Zhu, K. Olszewski, Y. Wu, P. Achlioptas, M. Chai, Y. Yan, and S. Tulyakov, “Quantized gan for complex music generation from dance videos,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVII . Springer, 2022, pp. 182–199

2022

Cited alongside, same era.

L. N. Ferreira, L. Mou, J. Whitehead, and L. H. Lelis, “Controlling perceived emotion in symbolic music generation with monte carlo tree search,” in Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment , vol. 18, no. 1, 2022, pp. 163–170

2022

Cited alongside, same era.

Later among the works it cites.

J. Liu, Y. Dong, Z. Cheng, X. Zhang, X. Li, F. Yu, and M. Sun, “Symphony generation with permutation invariant language model,” arXiv preprint arXiv:2205.05448 , 2022

Original

2022

Later among the works it cites.

A. Agostinelli, T. I. Denk, Z. Borsos, J. Engel, M. Verzetti, A. Caillon, Q. Huang, A. Jansen, A. Roberts, M. Tagliasacchi et al. , “Musiclm: Generating music from text,” arXiv preprint arXiv:2301.11325 , 2023

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2023

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Q. Huang, D. S. Park, T. Wang, T. I. Denk, A. Ly, N. Chen, Z. Zhang, Z. Zhang, J. Yu, C. Frank et al. , “Noise2music: Text-conditioned music generation with diffusion models,” arXiv preprint arXiv:2302.03917 , 2023

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2023

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P. Zhu, C. Pang, S. Wang, Y. Chai, Y. Sun, H. Tian, and H. Wu, “Ernie-music: Text-to-waveform music generation with diffusion models,” arXiv preprint arXiv:2302.04456 , 2023

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F. Schneider, Z. Jin, and B. Schölkopf, “Moûsai: Text-to-music generation with long-context latent diffusion,” arXiv preprint arXiv:2301.11757 , 2023

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OpenAI, “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

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S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al. , “Sparks of artificial general intelligence: Early experiments with gpt-4,” arXiv preprint arXiv:2303.12712 , 2023

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D. von Rütte, L. Biggio, Y. Kilcher, and T. Hofmann, “Figaro: Controllable music generation using learned and expert features,” in The Eleventh International Conference on Learning Representations , 2023

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P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023

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S. Di, Z. Jiang, S. Liu, Z. Wang, L. Zhu, Z. He, H. Liu, and S. Yan, “Video background music generation with controllable music transformer,” in Proceedings of the 29th ACM International Conference on Multimedia , ser. MM ’21. New York, NY, USA: Association for Computing Machinery, 2021, pp. 2037–2045

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