Cascaded co-attention between CLAP audio features and RoBERTa text features improves text-to-audio retrieval mAP by about 16 percent on Clotho and 15 percent on AudioCaps relative to the authors' earlier GPTtar method.
Language-based Audio Retrieval Task in DCASE 2022 Challenge
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abstract
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.
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Language-based Audio Retrieval with Co-Attention Networks
Cascaded co-attention between CLAP audio features and RoBERTa text features improves text-to-audio retrieval mAP by about 16 percent on Clotho and 15 percent on AudioCaps relative to the authors' earlier GPTtar method.