mlsanityData to 14 Oct 2026

Language-based Audio Retrieval Task in DCASE 2022 Challenge

arXiv 2209.09967 ↗First posted 20 Sep 2022eess.AS cs.SDcited by 5

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Abstract (arXiv)

Language-based audio retrieval is a task, where natural language textual captions are used as queries to retrieve audio signals from a dataset. It has been first introduced into DCASE 2022 Challenge as Subtask 6B of task 6, which aims at developing computational systems to model relationships between audio signals and free-form textual descriptions. Compared with audio captioning (Subtask 6A), which is about generating audio captions for audio signals, language-based audio retrieval (Subtask 6B) focuses on ranking audio signals according to their relevance to natural language textual captions. In DCASE 2022 Challenge, the provided baseline system for Subtask 6B was significantly outperformed, with top performance being 0.276 in mAP@10. This paper presents the outcome of Subtask 6B in terms of submitted systems' performance and analysis.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Sep 2022.

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Citing papers per year: 2023: 3; 2024: 2.

5 papers.

  1. 2023Advancing Natural-Language Based Audio Retrieval with PaSST and Large Audio-Caption Data Setscited by 6
  2. 2023Dynamic Convolutional Neural Networks as Efficient Pre-trained Audio Modelscited by 3
  3. 2024The language of sound search: Examining User Queries in Audio Search Enginescited by 3
  4. 2023Data leakage in cross-modal retrieval training: A case studycited by 1
  5. 2024Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairscited by 1