mlsanityData to 7 Oct 2026

Jukebox: A Generative Model for Music

arXiv 2005.00341 ↗First posted 30 Apr 2020eess.AS cs.LG cs.SDcited by 535

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Cited by 73 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at https://jukebox.openai.com, along with model weights and code at https://github.com/openai/jukebox

Key facts. Cited by 535 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2020.

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Citing papers per year: 2021: 62; 2022: 65; 2023: 110; 2024: 122; 2025: 112; 2026: 64.

535 papers.

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  5. 2021SoundStream: An End-to-End Neural Audio Codeccited by 756
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  7. 2022High Fidelity Neural Audio Compressioncited by 673
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  11. 2023T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representationscited by 577
  12. 2023Simple and Controllable Music Generationcited by 480
38 more
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  3. 2023High-Fidelity Audio Compression with Improved RVQGANcited by 423
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  7. 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
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  20. 2023MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Trainingcited by 175
  21. 2022Can Foundation Models Wrangle Your Data?cited by 173
  22. 2023Noise2Music: Text-conditioned Music Generation with Diffusion Modelscited by 173
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  37. 2021Generative Models as a Data Source for Multiview Representation Learningcited by 104
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