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Cited by 346

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Visually Indicated Sounds

Open access
Owens, Andrew, Isola, Phillip, McDermott, Josh, Torralba, Antonio, Adelson, Edward H., Freeman, William T.·2016·2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

Objects make distinctive sounds when they are hit or scratched. These sounds reveal aspects of an object's material properties, as well as the actions that produced them. In this paper, we propose the task of predicting what sound an object makes when struck as a way of studying physical interactions within a visual scene. We present an algorithm that synthesizes sound from silent videos of people hitting and scratching objects with a drumstick. This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions.

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Metadata

Authors
Owens, A., Isola, P., McDermott, J., Torralba, A., Adelson, E. H., & Freeman, W. T.
Venue
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2016-06
Pages
2405-2413
ISSN
1063-6919 (ISSN-L)
OpenAlex
W2196697617
Cited by
346
References
9
FWCI
38.57
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Visually Indicated Sounds. https://app.cochat.ai/papers/10.1109/cvpr.2016.264
The paper
Owens, A., Isola, P., McDermott, J., Torralba, A., Adelson, E. H., & Freeman, W. T. (2016). Visually Indicated Sounds. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.264

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.