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dc.contributor.authorDavis, Abe
dc.contributor.authorRubinstein, Michael
dc.contributor.authorWadhwa, Neal
dc.contributor.authorMysore, Gautham J.
dc.contributor.authorDurand, Fredo
dc.contributor.authorFreeman, William T.
dc.date.accessioned2015-11-24T14:22:15Z
dc.date.available2015-11-24T14:22:15Z
dc.date.issued2014-07
dc.identifier.issn07300301
dc.identifier.urihttp://hdl.handle.net/1721.1/100023
dc.description.abstractWhen sound hits an object, it causes small vibrations of the object's surface. We show how, using only high-speed video of the object, we can extract those minute vibrations and partially recover the sound that produced them, allowing us to turn everyday objects---a glass of water, a potted plant, a box of tissues, or a bag of chips---into visual microphones. We recover sounds from high-speed footage of a variety of objects with different properties, and use both real and simulated data to examine some of the factors that affect our ability to visually recover sound. We evaluate the quality of recovered sounds using intelligibility and SNR metrics and provide input and recovered audio samples for direct comparison. We also explore how to leverage the rolling shutter in regular consumer cameras to recover audio from standard frame-rate videos, and use the spatial resolution of our method to visualize how sound-related vibrations vary over an object's surface, which we can use to recover the vibration modes of an object.en_US
dc.description.sponsorshipQatar Computing Research Instituteen_US
dc.description.sponsorshipNational Science Foundation (U.S.) (CGV-1111415)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374)en_US
dc.description.sponsorshipMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.description.sponsorshipMicrosoft Research (PhD Fellowship)en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/2601097.2601119en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleThe visual microphone: Passive recovery of sound from videoen_US
dc.typeArticleen_US
dc.identifier.citationAbe Davis, Michael Rubinstein, Neal Wadhwa, Gautham J. Mysore, Fredo Durand, and William T. Freeman. 2014. The visual microphone: passive recovery of sound from video. ACM Trans. Graph. 33, 4, Article 79 (July 2014), 10 pages.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematicsen_US
dc.contributor.mitauthorDavis, Abeen_US
dc.contributor.mitauthorRubinstein, Michaelen_US
dc.contributor.mitauthorWadhwa, Nealen_US
dc.contributor.mitauthorDurand, Fredoen_US
dc.contributor.mitauthorFreeman, William T.en_US
dc.relation.journalACM Transactions on Graphicsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsDavis, Abe; Rubinstein, Michael; Wadhwa, Neal; Mysore, Gautham J.; Durand, Fredo; Freeman, William T.en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-3707-3807
dc.identifier.orcidhttps://orcid.org/0000-0003-1469-2696
dc.identifier.orcidhttps://orcid.org/0000-0002-2902-6752
dc.identifier.orcidhttps://orcid.org/0000-0001-9919-069X
dc.identifier.orcidhttps://orcid.org/0000-0002-2231-7995
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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