Visual Acoustic Matching

• URL: http://arxiv.org/abs/2202.06875v1
• Date: Mon, 14 Feb 2022 17:05:22 GMT
• Title: Visual Acoustic Matching
• Authors: Changan Chen, Ruohan Gao, Paul Calamia, Kristen Grauman
• Abstract summary: We introduce the visual acoustic matching task, in which an audio clip is transformed to sound like it was recorded in a target environment. Given an image of the target environment and a waveform for the source audio, the goal is to re-synthesize the audio to match the target room acoustics as suggested by its visible geometry and materials.
• Score: 92.91522122739845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce the visual acoustic matching task, in which an audio clip is transformed to sound like it was recorded in a target environment. Given an image of the target environment and a waveform for the source audio, the goal is to re-synthesize the audio to match the target room acoustics as suggested by its visible geometry and materials. To address this novel task, we propose a cross-modal transformer model that uses audio-visual attention to inject visual properties into the audio and generate realistic audio output. In addition, we devise a self-supervised training objective that can learn acoustic matching from in-the-wild Web videos, despite their lack of acoustically mismatched audio. We demonstrate that our approach successfully translates human speech to a variety of real-world environments depicted in images, outperforming both traditional acoustic matching and more heavily supervised baselines.

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