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Computer Science > Sound

arXiv:2305.17758 (cs)
[Submitted on 28 May 2023]

Title:CAPTDURE: Captioned Sound Dataset of Single Sources

Authors:Yuki Okamoto, Kanta Shimonishi, Keisuke Imoto, Kota Dohi, Shota Horiguchi, Yohei Kawaguchi
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Abstract:In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model training. However, when we collect the captions for multiple-source sound, it is not easy to collect detailed captions for each sound source, such as the number of sound occurrences and timbre. Therefore, it is difficult to extract only the single-source target sound by the model-training method using a conventional captioned sound dataset. In this work, we constructed a dataset with captions for a single-source sound named CAPTDURE, which can be used in various tasks such as environmental sound separation and synthesis. Our dataset consists of 1,044 sounds and 4,902 captions. We evaluated the performance of environmental sound extraction using our dataset. The experimental results show that the captions for single-source sounds are effective in extracting only the single-source target sound from the mixture sound.
Comments: Accepted to INTERSPEECH2023
Subjects: Sound (cs.SD); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2305.17758 [cs.SD]
  (or arXiv:2305.17758v1 [cs.SD] for this version)
  https://doi.org/10.48550/arXiv.2305.17758

Submission history

From: Yuki Okamoto [view email]
[v1] Sun, 28 May 2023 15:56:20 UTC (6,088 KB)
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