Audio Retrieval With Natural Language Queries: A Benchmark Study

The objectives of this work are cross-modal text-audio and audio-text retrieval , in which the goal is to retrieve the audio content from a pool of candidates that best matches a given written description and vice versa. Text-audio retrieval enables users to search large databases through an intuiti...

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Bibliographic Details
Published in:IEEE transactions on multimedia Vol. 25; pp. 2675 - 2685
Main Authors: Koepke, A. Sophia, Oncescu, Andreea-Maria, Henriques, Joao F., Akata, Zeynep, Albanie, Samuel
Format: Journal Article
Language:English
Published: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Audio retrieval
Benchmark testing
Benchmarks
datasets
Descriptions
Free form
Grounding
Metadata
Natural language
Natural languages
Queries
Retrieval
Task analysis
text-based retrieval
Visual databases
Visualization
ISSN:1520-9210, 1941-0077
Online Access:Get full text
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Summary:The objectives of this work are cross-modal text-audio and audio-text retrieval , in which the goal is to retrieve the audio content from a pool of candidates that best matches a given written description and vice versa. Text-audio retrieval enables users to search large databases through an intuitive interface: they simply issue free-form natural language descriptions of the sound they would like to hear. To study the tasks of text-audio and audio-text retrieval, which have received limited attention in the existing literature, we introduce three challenging new benchmarks. We first construct text-audio and audio-text retrieval benchmarks from the AudioCaps and Clotho audio captioning datasets. Additionally, we introduce the SoundDescs benchmark, which consists of paired audio and natural language descriptions for a diverse collection of sounds that are complementary to those found in AudioCaps and Clotho . We employ these three benchmarks to establish baselines for cross-modal text-audio and audio-text retrieval, where we demonstrate the benefits of pre-training on diverse audio tasks. We hope that our benchmarks will inspire further research into audio retrieval with free-form text queries.
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ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2022.3149712