Conference item
Audio retrieval with natural language queries
- Abstract:
- We consider the task of retrieving audio using free-form natural language queries. To study this problem, which has received limited attention in the existing literature, we introduce challenging new benchmarks for text-based audio retrieval using text annotations sourced from the AudioCaps and Clotho datasets. We then employ these benchmarks to establish baselines for cross-modal audio retrieval, where we demonstrate the benefits of pre-training on diverse audio tasks. We hope that our benchmarks will inspire further research into cross-modal text-based audio retrieval with free-form text queries.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 1.3MB, Terms of use)
-
- Publisher copy:
- 10.21437/Interspeech.2021-2227
Authors
- Publisher:
- International Speech Communication Association
- Host title:
- Proceedings of Interspeech 2021
- Pages:
- 2411-2415
- Publication date:
- 2021-08-30
- Acceptance date:
- 2020-07-24
- Event title:
- Interspeech 2021
- Event location:
- Brno, Czechia
- Event website:
- https://www.interspeech2021.org/
- Event start date:
- 2021-08-30
- Event end date:
- 2021-09-03
- DOI:
- EISSN:
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2308-457X
- ISSN:
-
1990-9772
- ISBN:
- 9781713836902
- Language:
-
English
- Keywords:
- Pubs id:
-
1241851
- Local pid:
-
pubs:1241851
- Deposit date:
-
2022-06-10
- ARK identifier:
Terms of use
- Copyright holder:
- International Speech Communication Association
- Copyright date:
- 2021
- Rights statement:
- Copyright © 2021 ISCA.
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