Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916745145483264 |
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| author | Ick, Christopher Wichern, Gordon Masuyama, Yoshiki Germain, François Roux, Jonathan Le |
| author_facet | Ick, Christopher Wichern, Gordon Masuyama, Yoshiki Germain, François Roux, Jonathan Le |
| contents | The characteristics of a sound field are intrinsically linked to the geometric and spatial properties of the environment surrounding a sound source and a listener. The physics of sound propagation is captured in a time-domain signal known as a room impulse response (RIR). Prior work using neural fields (NFs) has allowed learning spatially-continuous representations of RIRs from finite RIR measurements. However, previous NF-based methods have focused on monaural omnidirectional or at most binaural listeners, which does not precisely capture the directional characteristics of a real sound field at a single point. We propose a direction-aware neural field (DANF) that more explicitly incorporates the directional information by Ambisonic-format RIRs. While DANF inherently captures spatial relations between sources and listeners, we further propose a direction-aware loss. In addition, we investigate the ability of DANF to adapt to new rooms in various ways including low-rank adaptation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_13617 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses Ick, Christopher Wichern, Gordon Masuyama, Yoshiki Germain, François Roux, Jonathan Le Audio and Speech Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Sound The characteristics of a sound field are intrinsically linked to the geometric and spatial properties of the environment surrounding a sound source and a listener. The physics of sound propagation is captured in a time-domain signal known as a room impulse response (RIR). Prior work using neural fields (NFs) has allowed learning spatially-continuous representations of RIRs from finite RIR measurements. However, previous NF-based methods have focused on monaural omnidirectional or at most binaural listeners, which does not precisely capture the directional characteristics of a real sound field at a single point. We propose a direction-aware neural field (DANF) that more explicitly incorporates the directional information by Ambisonic-format RIRs. While DANF inherently captures spatial relations between sources and listeners, we further propose a direction-aware loss. In addition, we investigate the ability of DANF to adapt to new rooms in various ways including low-rank adaptation. |
| title | Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses |
| topic | Audio and Speech Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Sound |
| url | https://arxiv.org/abs/2505.13617 |