Region-Specific Audio Tagging for Spatial Sound
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866911150238597120 |
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| author | Zhao, Jinzheng Xu, Yong Liu, Haohe Berghi, Davide Qian, Xinyuan Kong, Qiuqiang Zhao, Junqi Plumbley, Mark D. Wang, Wenwu |
| author_facet | Zhao, Jinzheng Xu, Yong Liu, Haohe Berghi, Davide Qian, Xinyuan Kong, Qiuqiang Zhao, Junqi Plumbley, Mark D. Wang, Wenwu |
| contents | Audio tagging aims to label sound events appearing in an audio recording. In this paper, we propose region-specific audio tagging, a new task which labels sound events in a given region for spatial audio recorded by a microphone array. The region can be specified as an angular space or a distance from the microphone. We first study the performance of different combinations of spectral, spatial, and position features. Then we extend state-of-the-art audio tagging systems such as pre-trained audio neural networks (PANNs) and audio spectrogram transformer (AST) to the proposed region-specific audio tagging task. Experimental results on both the simulated and the real datasets show the feasibility of the proposed task and the effectiveness of the proposed method. Further experiments show that incorporating the directional features is beneficial for omnidirectional tagging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09526 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Region-Specific Audio Tagging for Spatial Sound Zhao, Jinzheng Xu, Yong Liu, Haohe Berghi, Davide Qian, Xinyuan Kong, Qiuqiang Zhao, Junqi Plumbley, Mark D. Wang, Wenwu Audio and Speech Processing Sound Audio tagging aims to label sound events appearing in an audio recording. In this paper, we propose region-specific audio tagging, a new task which labels sound events in a given region for spatial audio recorded by a microphone array. The region can be specified as an angular space or a distance from the microphone. We first study the performance of different combinations of spectral, spatial, and position features. Then we extend state-of-the-art audio tagging systems such as pre-trained audio neural networks (PANNs) and audio spectrogram transformer (AST) to the proposed region-specific audio tagging task. Experimental results on both the simulated and the real datasets show the feasibility of the proposed task and the effectiveness of the proposed method. Further experiments show that incorporating the directional features is beneficial for omnidirectional tagging. |
| title | Region-Specific Audio Tagging for Spatial Sound |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2509.09526 |