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dc.contributor.authorChourdakis, ETen_US
dc.contributor.authorReiss, JDen_US
dc.date.accessioned2017-02-21T14:03:39Z
dc.date.available2017-01-18en_US
dc.date.issued2017en_US
dc.date.submitted2017-02-14T12:13:29.879Z
dc.identifier.issn1549-4950en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/19457
dc.format.extent56 - 65en_US
dc.relation.ispartofJOURNAL OF THE AUDIO ENGINEERING SOCIETYen_US
dc.rightsThis is a pre-copyedited, author-produced version of an article accepted for publication in JAES following peer review. The version of record is available http://www.aes.org/e-lib/browse.cfm?elib=18543
dc.titleA Machine-Learning Approach to Application of Intelligent Artificial Reverberationen_US
dc.typeArticle
dc.rights.holder© 2017 Audio Engineering Society
dc.identifier.doi10.17743/jaes.2016.0069en_US
pubs.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000395498300007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.issue1-2en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume65en_US


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