MAGIC: Map-Guided Few-Shot Audio-Visual Acoustics Modeling
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916257400356864 |
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| author | Huang, Diwei Lin, Kunyang Chen, Peihao Du, Qing Tan, Mingkui |
| author_facet | Huang, Diwei Lin, Kunyang Chen, Peihao Du, Qing Tan, Mingkui |
| contents | Few-shot audio-visual acoustics modeling seeks to synthesize the room impulse response in arbitrary locations with few-shot observations. To sufficiently exploit the provided few-shot data for accurate acoustic modeling, we present a *map-guided* framework by constructing acoustic-related visual semantic feature maps of the scenes. Visual features preserve semantic details related to sound and maps provide explicit structural regularities of sound propagation, which are valuable for modeling environment acoustics. We thus extract pixel-wise semantic features derived from observations and project them into a top-down map, namely the **observation semantic map**. This map contains the relative positional information among points and the semantic feature information associated with each point. Yet, limited information extracted by few-shot observations on the map is not sufficient for understanding and modeling the whole scene. We address the challenge by generating a **scene semantic map** via diffusing features and anticipating the observation semantic map. The scene semantic map then interacts with echo encoding by a transformer-based encoder-decoder to predict RIR for arbitrary speaker-listener query pairs. Extensive experiments on Matterport3D and Replica dataset verify the efficacy of our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13860 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAGIC: Map-Guided Few-Shot Audio-Visual Acoustics Modeling Huang, Diwei Lin, Kunyang Chen, Peihao Du, Qing Tan, Mingkui Computer Vision and Pattern Recognition Few-shot audio-visual acoustics modeling seeks to synthesize the room impulse response in arbitrary locations with few-shot observations. To sufficiently exploit the provided few-shot data for accurate acoustic modeling, we present a *map-guided* framework by constructing acoustic-related visual semantic feature maps of the scenes. Visual features preserve semantic details related to sound and maps provide explicit structural regularities of sound propagation, which are valuable for modeling environment acoustics. We thus extract pixel-wise semantic features derived from observations and project them into a top-down map, namely the **observation semantic map**. This map contains the relative positional information among points and the semantic feature information associated with each point. Yet, limited information extracted by few-shot observations on the map is not sufficient for understanding and modeling the whole scene. We address the challenge by generating a **scene semantic map** via diffusing features and anticipating the observation semantic map. The scene semantic map then interacts with echo encoding by a transformer-based encoder-decoder to predict RIR for arbitrary speaker-listener query pairs. Extensive experiments on Matterport3D and Replica dataset verify the efficacy of our framework. |
| title | MAGIC: Map-Guided Few-Shot Audio-Visual Acoustics Modeling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.13860 |