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M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, “Rwc music database: Popular, classical and jazz music databases,” in Proceedings of the 3rd International Society for Music Information Retrieval Conference , 2002
2002
Earlier work this paper cites.
S. Böck, F. Korzeniowski, J. Schlüter, F. Krebs, and G. Widmer, “Madmom: A new python audio and music signal processing library,” in Proceedings of the 24th ACM international conference on Multimedia , 2016, pp. 1174–1178
2016
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A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017
2017
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Z. Rafii, A. Liutkus, F.-R. Stöter, S. I. Mimilakis, and R. Bittner, “Musdb18-a corpus for music separation,” 2017
2017
Earlier work this paper cites.
K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fréchet audio distance: A metric for evaluating music enhancement algorithms,” arXiv preprint arXiv:1812.08466 , 2018
Original
2018
Earlier work this paper cites.
J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT , 2019, pp. 4171–4186
2019
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
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J. Jiang, K. Chen, W. Li, and G. Xia, “Large-vocabulary chord transcription via chord structure decomposition,” in Proceedings of the 20th International Society for Music Information Retrieval Conference , A. Flexer, G. Peeters, J. Urbano, and A. Volk, Eds., 2019, pp. 644–651
2019
Earlier work this paper cites.
A. Défossez, N. Usunier, L. Bottou, and F. Bach, “Demucs: Deep extractor for music sources with extra unlabeled data remixed,” arXiv preprint arXiv:1909.01174 , 2019
Original
2019
Earlier work this paper cites.
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever, “Jukebox: A generative model for music,” arXiv preprint arXiv:2005.00341 , 2020
Original
2020
Earlier work this paper cites.

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C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
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X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021, pp. 4582–4597
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2021
Cited alongside, same era.
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2023

Content-based Controls For Music Large Language Modeling

Lin, Liwei, Xia, Gus, Jiang, Junyan et al.

Understand

Recent years have witnessed a rapid growth of large-scale language models in the domain of music audio.

Such models enable end-to-end generation of higher-quality music, and some allow conditioned generation using text descriptions.
However, the control power of text controls on music is intrinsically limited, as they can only describe music indirectly through meta-data (such as singers and instruments) or high-level representations (such as genre and emotion).
We aim to further equip the models with direct and content-based controls on innate music languages such as pitch, chords and drum track.

Built on

M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka, “Rwc music database: Popular, classical and jazz music databases,” in Proceedings of the 3rd International Society for Music Information Retrieval Conference , 2002
2002
Earlier work this paper cites.
S. Böck, F. Korzeniowski, J. Schlüter, F. Krebs, and G. Widmer, “Madmom: A new python audio and music signal processing library,” in Proceedings of the 24th ACM international conference on Multimedia , 2016, pp. 1174–1178
2016
Earlier work this paper cites.
A. Van Den Oord, O. Vinyals et al. , “Neural discrete representation learning,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Z. Rafii, A. Liutkus, F.-R. Stöter, S. I. Mimilakis, and R. Bittner, “Musdb18-a corpus for music separation,” 2017
2017
Earlier work this paper cites.
K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi, “Fréchet audio distance: A metric for evaluating music enhancement algorithms,” arXiv preprint arXiv:1812.08466 , 2018
Original
2018
Earlier work this paper cites.
J. D. M.-W. C. Kenton and L. K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of NAACL-HLT , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
J. Jiang, K. Chen, W. Li, and G. Xia, “Large-vocabulary chord transcription via chord structure decomposition,” in Proceedings of the 20th International Society for Music Information Retrieval Conference , A. Flexer, G. Peeters, J. Urbano, and A. Volk, Eds., 2019, pp. 644–651
2019
Earlier work this paper cites.
A. Défossez, N. Usunier, L. Bottou, and F. Bach, “Demucs: Deep extractor for music sources with extra unlabeled data remixed,” arXiv preprint arXiv:1909.01174 , 2019
Original
2019
Earlier work this paper cites.
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever, “Jukebox: A generative model for music,” arXiv preprint arXiv:2005.00341 , 2020
Original
2020
Earlier work this paper cites.

Similar

C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Cited alongside, same era.
X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , 2021, pp. 4582–4597
2021
Cited alongside, same era.
E. J. Hu, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, W. Chen et al. , “Lora: Low-rank adaptation of large language models,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
N. Zeghidour, A. Luebs, A. Omran, J. Skoglund, and M. Tagliasacchi, “Soundstream: An end-to-end neural audio codec,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 30, pp. 495–507, 2021

Then

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

2021

Cited alongside, same era.

Y.-T. Wu, Y.-J. Luo, T.-P. Chen, I. Wei, J.-Y. Hsu, Y.-C. Chuang, L. Su et al. , “Omnizart: A general toolbox for automatic music transcription,” The Journal of Open Source Software , vol. 6, no. 68, p. 3391, 2021

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Y.-T. Wu, Y.-J. Luo, T.-P. Chen, I.-C. Wei, J.-Y. Hsu, Y.-C. Chuang, and L. Su, “Omnizart: A general toolbox for automatic music transcription,” Journal of Open Source Software , vol. 6, no. 68, p. 3391, 2021. [Online]. Available: https://doi.org/10.21105/joss.03391

2021

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A. Guzhov, F. Raue, J. Hees, and A. Dengel, “Audioclip: Extending clip to image, text and audio,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 976–980

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,” in Proceedings of the 23rd International Society for Music Information Retrieval Conference , P. Rao, H. A. Murthy, A. Srinivasamurthy, R. M. Bittner, R. C. Repetto, M. Goto, X. Serra, and M. Miron, Eds., 2022, pp. 559–566

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A. Défossez, J. Copet, G. Synnaeve, and Y. Adi, “High fidelity neural audio compression,” arXiv preprint arXiv:2210.13438 , 2022

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X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang, “P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) , 2022, pp. 61–68

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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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C. Donahue, A. Caillon, A. Roberts, E. Manilow, P. Esling, A. Agostinelli, M. Verzetti, I. Simon, O. Pietquin, N. Zeghidour et al. , “Singsong: Generating musical accompaniments from singing,” arXiv preprint arXiv:2301.12662 , 2023

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