title: A Large-scale Dataset for Audio-Language Representation Learning
summary: The AI community has made significant strides in developing powerful
  foundation models, driven by large-scale multimodal datasets. However, in the
  audio representation learning community, the present audio-language datasets
  suffer from limitations such as insufficient volume, simplistic content, and
  arduous collection procedures. To tackle these challenges, we present an
  innovative and automatic audio caption generation pipeline based on a series of
  public tools or APIs, and construct a large-scale, high-quality, audio-language
  dataset, named as Auto-ACD, comprising over 1.9M audio-text pairs. To
  demonstrate the effectiveness of the proposed dataset, we train popular models
  on our dataset and show performance improvement on various downstream tasks,
  namely, audio-language retrieval, audio captioning, environment classification.
  In addition, we establish a novel test set and provide a benchmark for
  audio-text tasks. The proposed dataset will be released at
  https://auto-acd.github.io/.
paper_authors._id: 650bd248b92f35bd37d263c1
paper_authors.name: Luoyi Sun
paper_authors._id: 650bd248b92f35bd37d263c2
paper_authors.name: Xuenan Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-21T09:50:04.592Z
paper_authors._id: 6457623c90bf61c87f9ffdc4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6457623c90bf61c87f9ffdc4/pKG76YipWfYFhSnorepsl.jpeg
paper_authors.fullname: Xuenan Xu
paper_authors.type: user
paper_authors.user: wsntxxn
paper_authors._id: 650bd248b92f35bd37d263c3
paper_authors.name: Mengyue Wu
paper_authors._id: 650bd248b92f35bd37d263c4
paper_authors.name: Weidi Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-21T09:50:24.463Z
paper_authors._id: 63f1841f2f7c0152e8748ec4
paper_authors.avatarUrl: /avatars/5398658c7d0cf556531a625a4ca5d18a.svg
paper_authors.fullname: Xie
paper_authors.type: user
paper_authors.user: Weidi
paper_title: A Large-scale Dataset for Audio-Language Representation Learning
paper_summary: The AI community has made significant strides in developing powerful
  foundation models, driven by large-scale multimodal datasets. However, in the
  audio representation learning community, the present audio-language datasets
  suffer from limitations such as insufficient volume, simplistic content, and
  arduous collection procedures. To tackle these challenges, we present an
  innovative and automatic audio caption generation pipeline based on a series of
  public tools or APIs, and construct a large-scale, high-quality, audio-language
  dataset, named as Auto-ACD, comprising over 1.9M audio-text pairs. To
  demonstrate the effectiveness of the proposed dataset, we train popular models
  on our dataset and show performance improvement on various downstream tasks,
  namely, audio-language retrieval, audio captioning, environment classification.
  In addition, we establish a novel test set and provide a benchmark for
  audio-text tasks. The proposed dataset will be released at
  https://auto-acd.github.io/.
paper_ai_keywords: audio caption generation public tools APIs large-scale, high-quality, audio-language dataset Auto-ACD audio-language retrieval audio captioning environment classification benchmark for audio-text tasks