Detecting Hands from Piano MIDI Data
Abstract
When 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.
- Citation
- BibTeX
Hadjakos, A., Waloschek, S. & Leemhuis, A.,
(2019).
Detecting Hands from Piano MIDI Data.
Mensch und Computer 2019 - Workshopband.
Bonn:
Gesellschaft für Informatik e.V..
DOI: 10.18420/muc2019-ws-578
@inproceedings{mci/Hadjakos2019,
author = {Hadjakos, Aristotelis AND Waloschek, Simon AND Leemhuis, Alexander},
title = {Detecting Hands from Piano MIDI Data},
booktitle = {Mensch und Computer 2019 - Workshopband},
year = {2019},
editor = {} ,
doi = { 10.18420/muc2019-ws-578 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
author = {Hadjakos, Aristotelis AND Waloschek, Simon AND Leemhuis, Alexander},
title = {Detecting Hands from Piano MIDI Data},
booktitle = {Mensch und Computer 2019 - Workshopband},
year = {2019},
editor = {} ,
doi = { 10.18420/muc2019-ws-578 },
publisher = {Gesellschaft für Informatik e.V.},
address = {Bonn}
}
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xmlui.MetaDataDisplay.field.date: 2019
Language:
(en)

Content Type: Text/Conference Poster
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- Workshopband MuC 2019 [137]