Cited by 96Speech Recognition and SynthesisMultimodal Machine Learning ApplicationsMusic and Audio Processing

AudioCaps: Generating Captions for Audios in The Wild

Open access
Kim, Chris Dongjoo, Kim, Byeongchang, Lee, Hyunmin, Kim, Gunhee·2019·Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Abstract

Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, Gunhee Kim. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

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Crossref · 10.18653/v1/n19-1011

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  • PANN is a CNN-based audio classification model with 7 downsampling and 7 upsampling blocks.

    “PANN is a CNN-based [19] audio classification model with 7 downsampling CNN blocks and 7 upsampling blocks.”

    From Large-Scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation (2023) · cites this paper as [10]

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References 57 · Cited by 96

Research works printed but not linked: 4

  1. AudioCaps: Generating Captions for Audios in The Wild https://aclanthology.org/N19-1011/ (Kim et al., NAACL 2019)
  2. AudioCaps: Generating Captions for Audios in The Wild (Kim et al., NAACL 2019)
  3. Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, and Gunhee Kim. 2019. AudioCaps: Generating Captions for Audios in The Wild. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 119–132, Minneapolis, Minnesota. Association for Computational Linguistics.
  4. ACL materials are Copyright © 1963–2026 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.

Authors
Kim, C. D., Kim, B., Lee, H., & Kim, G.
Venue
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Published
2019
Pages
119-132
OpenAlex
W2945761034
Cited by
96
References
57
Citations by year
2026: 20 · 2025: 26 · 2024: 10 · 2023: 8 · 2022: 8 · 2021: 14 · 2020: 8 · 2019: 2
FWCI
3.06
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
  • Seoul National University Open Repository (Seoul National University) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Keywords
audio captioning, audio description, automatic caption generation, speech-to-text

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