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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2501.10755 |
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| _version_ | 1866910789429886976 |
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| author | Dong, Yuxuan Wang, Qing Hong, Hengyi Jiang, Ya Cheng, Shi |
| author_facet | Dong, Yuxuan Wang, Qing Hong, Hengyi Jiang, Ya Cheng, Shi |
| contents | In traditional sound event localization and detection (SELD) tasks, the focus is typically on sound event detection (SED) and direction-of-arrival (DOA) estimation, but they fall short of providing full spatial information about the sound source. The 3D SELD task addresses this limitation by integrating source distance estimation (SDE), allowing for complete spatial localization. We propose three approaches to tackle this challenge: a novel method with independent training and joint prediction, which firstly treats DOA and distance estimation as separate tasks and then combines them to solve 3D SELD; a dual-branch representation with source Cartesian coordinate used for simultaneous DOA and distance estimation; and a three-branch structure that jointly models SED, DOA, and SDE within a unified framework. Our proposed method ranked first in the DCASE 2024 Challenge Task 3, demonstrating the effectiveness of joint modeling for addressing the 3D SELD task. The relevant code for this paper will be open-sourced in the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10755 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation Dong, Yuxuan Wang, Qing Hong, Hengyi Jiang, Ya Cheng, Shi Sound Machine Learning Audio and Speech Processing In traditional sound event localization and detection (SELD) tasks, the focus is typically on sound event detection (SED) and direction-of-arrival (DOA) estimation, but they fall short of providing full spatial information about the sound source. The 3D SELD task addresses this limitation by integrating source distance estimation (SDE), allowing for complete spatial localization. We propose three approaches to tackle this challenge: a novel method with independent training and joint prediction, which firstly treats DOA and distance estimation as separate tasks and then combines them to solve 3D SELD; a dual-branch representation with source Cartesian coordinate used for simultaneous DOA and distance estimation; and a three-branch structure that jointly models SED, DOA, and SDE within a unified framework. Our proposed method ranked first in the DCASE 2024 Challenge Task 3, demonstrating the effectiveness of joint modeling for addressing the 3D SELD task. The relevant code for this paper will be open-sourced in the future. |
| title | An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.10755 |