Few-Shot Audio-Visual Learning of Environment Acoustics

Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman

Keywords: Audio-Visual Learning, RIR prediction

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

TL;DR: This work explores how to infer RIRs based on few-shot images and echoes observed in a 3D environment. Our novel transformer-based model outperforms state-of-the-art methods, while also generalizing to unseen 3D spaces.