AudioLM: A Language Modeling Approach to Audio Generation-Reference-Cited by-同舟云学术

AudioLM: A Language Modeling Approach to Audio Generation

Published:2023 Issue: Volume:31 Page:2523-2533
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Borsos Zalán¹^ORCID,Marinier Raphaël²,Vincent Damien¹,Kharitonov Eugene²,Pietquin Olivier²,Sharifi Matt¹^ORCID,Roblek Dominik¹^ORCID,Teboul Olivier³,Grangier David⁴^ORCID,Tagliasacchi Marco¹^ORCID,Zeghidour Neil²^ORCID

Affiliation:

1. Google, Research, Zurich, Switzerland

2. Google, Research, Paris, France

3. Google, Research, Paris, CA, France

4. Google, Research, Mountain View, CA, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/9970249/10158503.pdf?arnumber=10158503

Reference64 articles.

1. The Zero Resource Speech Challenge 2021: Spoken Language Modelling

2. Self-supervised audio representation learning for mobile devices;tagliasacchi,2019

3. Scaling autoregressive models for content-rich text-to-image generation;yu;Trans Mach Learn Res,0

4. Hubert: How Much Can a Bad Teacher Benefit ASR Pre-Training?

5. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,0

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