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Symbolic music generation aims to create musical notes, which can help users compose music, such as generating target instrument tracks based on provided source tracks.
On information and sufficiency
Solomon Kullback and Richard A Leibler · 1951
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Learning long-term dependencies with gradient descent is difficult
Y. Bengio, P. Simard, and P. Frasconi · 1994
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The abc music standard 2.1
Walshaw Christopher · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment, 2017
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Li-Chia Yang, Szu-Yu Chou, and Yi-Hsuan Yang · 2017
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Midi miner - A python library for tonal tension and track classification
Rui Guo, Dorien Herremans, and Thor Magnusson · 2019
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MMM : Exploring conditional multi-track music generation with the transformer
Jeffrey Ens and Philippe Pasquier · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Pop music transformer: Beat-based modeling and generation of expressive pop piano compositions
Yu-Siang Huang and Yi-Hsuan Yang · 2020
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Popmag: Pop music accompaniment generation
Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, and Tie-Yan Liu · 2020
Cited alongside, same era.
Songmass: Automatic song writing with pre-training and alignment constraint
Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, and Tao Qin · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Classifier-free diffusion guidance
Symbolic music generation with diffusion models, 2021
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon · 2021
Later among the works it cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu · 2021
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MusicBERT: Symbolic music understanding with large-scale pre-training
Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, and Tie-Yan Liu · 2021
Later among the works it cites.
Re-creation of creations: A new paradigm for lyric-to-melody generation, 2022
Ang Lv, Xu Tan, Tao Qin, Tie-Yan Liu, and Rui Yan · 2022
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Museformer: Transformer with fine- and coarse-grained attention for music generation
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Jonathan Ho and Tim Salimans · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs
Wen-Yi Hsiao, Jen-Yu Liu, Yin-Cheng Yeh, and Yi-Hsuan Yang · 2021
Cited alongside, same era.
Telemelody: Lyric-to-melody generation with a template-based two-stage method
Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiangyang Li, Tao Qin, and Tie-Yan Liu · 2021
Cited alongside, same era.
Botao Yu, Peiling Lu, Rui Wang, Wei Hu, Xu Tan, Wei Ye, Shikun Zhang, Tao Qin, and Tie-Yan Liu · 2022
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Sdmuse: Stochastic differential music editing and generation via hybrid representation, 2022
Chen Zhang, Yi Ren, Kejun Zhang, and Shuicheng Yan · 2022
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Multitrack music transformer
Hao-Wen Dong, Ke Chen, Shlomo Dubnov, Julian McAuley, and Taylor Berg-Kirkpatrick · 2023
Closest in time.
Melodydiffusion: Chord-conditioned melody generation using a transformer-based diffusion model
Li Shuyu and Yunsick Sung · 2023
Closest in time.