Few-Shot Audio-Visual Learning of Environment Acoustics

• URL: http://arxiv.org/abs/2206.04006v1
• Date: Wed, 8 Jun 2022 16:38:24 GMT
• Title: Few-Shot Audio-Visual Learning of Environment Acoustics
• Authors: Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman
• Abstract summary: Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener. We explore how to infer RIRs based on a sparse set of images and echoes observed in the space. In experiments using a state-of-the-art audio-visual simulator for 3D environments, we demonstrate that our method successfully generates arbitrary RIRs.
• Score: 89.16560042178523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener, with implications for various applications in AR, VR, and robotics. Whereas traditional methods to estimate RIRs assume dense geometry and/or sound measurements throughout the environment, we explore how to infer RIRs based on a sparse set of images and echoes observed in the space. Towards that goal, we introduce a transformer-based method that uses self-attention to build a rich acoustic context, then predicts RIRs of arbitrary query source-receiver locations through cross-attention. Additionally, we design a novel training objective that improves the match in the acoustic signature between the RIR predictions and the targets. In experiments using a state-of-the-art audio-visual simulator for 3D environments, we demonstrate that our method successfully generates arbitrary RIRs, outperforming state-of-the-art methods and--in a major departure from traditional methods--generalizing to novel environments in a few-shot manner. Project: http://vision.cs.utexas.edu/projects/fs_rir.

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