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dc.contributor.authorMartinez Ramirez, M
dc.contributor.authorStoller, D
dc.contributor.authorMoffat, David
dc.date.accessioned2020-09-17T11:12:36Z
dc.date.issued2021-03
dc.identifier.issn0004-7554
dc.identifier.urihttp://hdl.handle.net/10026.1/16380
dc.description.abstract

The development of intelligent music production tools has been of growing interest in recent years. Deep learning approaches have been shown as being a highly effective method for approximating individual audio effects. In this work, we propose an end-to-end deep neural network based on the Wave-U-Net to perform automatic mixing of drums. We follow an end-to-end approach where raw audio from the individual drum recordings is the input of the system and the waveform of the stereo mix is the output. We compare the system to existing machine learning approaches to intelligent drum mixing. Through a subjective listening test we explore the performance of these systems when processing various types of drum mixes. We report that the mixes generated by our model are virtually indistinguishable from professional human mixes while also outperforming previous intelligent mixing approaches.

dc.format.extent142-151
dc.language.isoen
dc.publisherAudio Engineering Society
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleA Deep Learning Approach to Intelligent Drum Mixing with the Wave-U-Net
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000632042200002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue3
plymouth.volume69
plymouth.publisher-urlhttps://www.aes.org/e-lib/browse.cfm?elib=21023
plymouth.publication-statusPublished online
plymouth.journalJournal of the Audio Engineering Society
dc.identifier.doi10.17743/jaes.2020.0031
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Arts, Humanities and Business
dcterms.dateAccepted2020-09-01
dc.rights.embargodate2021-3-24
dc.rights.embargoperiodNot known
rioxxterms.versionAccepted Manuscript
rioxxterms.versionofrecord10.17743/jaes.2020.0031
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2021-03
rioxxterms.typeJournal Article/Review


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