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Measuring the acoustic characteristics of a space is often done by capturingits impulse response (IR), a representation of how a full-range stimulus soundexcites it. This work generates an IR from a single image, which can then beapplied to other signals using convolution, simulating the reverberantcharacteristics of the space shown in the image. Recording these IRs is bothtime-intensive and expensive, and often infeasible for inaccessible locations.We use an end-to-end neural network architecture to generate plausible audioimpulse responses from single images of acoustic environments. We evaluate ourmethod both by comparisons to ground truth data and by human expert evaluation.We demonstrate our approach by generating plausible impulse responses fromdiverse settings and formats including well known places, musical halls, roomsin paintings, images from animations and computer games, synthetic environmentsgenerated from text, panoramic images, and video conference backgrounds.

Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis