Language-based Audio Moment Retrieval

• URL: http://arxiv.org/abs/2409.15672v1
• Date: Tue, 24 Sep 2024 02:24:48 GMT
• Title: Language-based Audio Moment Retrieval
• Authors: Hokuto Munakata, Taichi Nishimura, Shota Nakada, Tatsuya Komatsu,
• Abstract summary: We propose and design a new task called audio moment retrieval (AMR) Unlike conventional language-based audio retrieval tasks, AMR aims to predict relevant moments in untrimmed long audio based on a text query. We build a dedicated dataset, Clotho-Moment, consisting of large-scale simulated audio recordings with moment annotations. We then propose a DETR-based model, named Audio Moment DETR (AM-DETR), as a fundamental framework for AMR tasks.
• Score: 14.227865973426843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose and design a new task called audio moment retrieval (AMR). Unlike conventional language-based audio retrieval tasks that search for short audio clips from an audio database, AMR aims to predict relevant moments in untrimmed long audio based on a text query. Given the lack of prior work in AMR, we first build a dedicated dataset, Clotho-Moment, consisting of large-scale simulated audio recordings with moment annotations. We then propose a DETR-based model, named Audio Moment DETR (AM-DETR), as a fundamental framework for AMR tasks. This model captures temporal dependencies within audio features, inspired by similar video moment retrieval tasks, thus surpassing conventional clip-level audio retrieval methods. Additionally, we provide manually annotated datasets to properly measure the effectiveness and robustness of our methods on real data. Experimental results show that AM-DETR, trained with Clotho-Moment, outperforms a baseline model that applies a clip-level audio retrieval method with a sliding window on all metrics, particularly improving Recall1@0.7 by 9.00 points. Our datasets and code are publicly available in https://h-munakata.github.io/Language-based-Audio-Moment-Retrieval.

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