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dc.contributor.authorHadjakos, Aristotelis
dc.contributor.authorWaloschek, Simon
dc.contributor.authorLeemhuis, Alexander
dc.date.accessioned2019-09-05T01:06:38Z
dc.date.available2019-09-05T01:06:38Z
dc.date.issued2019
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/25209
dc.description.abstractWhen a pianist is playing on a MIDI keyboard, the computer does not know with which hand a key was pressed. With the help of a Recurrent Neural Network (RNN), we assign played MIDI notes to one of the two hands. We compare our new approach with an existing heuristic algorithm and show that RNNs perform better. The solution is real-time capable and can be used via OSC from any programming environment. A non real-time capable variant provides slightly higher accuracy. Our solution can be used in music notation software to assign the left or right hand to the upper or lower staff automatically. Another application is live playing, where different synthesizer sounds can be mapped to the left and right hand.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2019 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.subjectpiano
dc.subjectmusic
dc.subjectdatasets
dc.subjectneural networks
dc.titleDetecting Hands from Piano MIDI Dataen
dc.typeText/Conference Poster
dc.pubPlaceBonn
mci.document.qualitydigidoc
mci.conference.sessiontitleMCI-WS23: Innovative Computerbasierte Musikinterfaces (ICMI)
mci.conference.locationHamburg
mci.conference.date8.-11. September 2019
dc.identifier.doi10.18420/muc2019-ws-578


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