Erdem, CagriLan, QichaoFuhrer, JulianMartin, CharlesTorresen, JimJensenius, Alexander RefsumSpagnol, SimoneValle, Andrea2023-07-122023-07-12June 24th978-88-945415-0-2http://hdl.handle.net/1885/294175In acoustic instruments, sound production relies on the interaction between physical objects. Digital musical instruments, on the other hand, are based on arbitrarily designed action-sound mappings. This paper describes the ongoing exploration of an empirically-based approach for simulating guitar playing technique when designing the mappings of 'air instruments'. We present results from an experiment in which 33 electric guitarists performed a set of basic sound-producing actions: impulsive, sustained, and iterative. The dataset consists of bioelectric muscle signals, motion capture, video, and audio recordings. This multimodal dataset was used to train a long short-term memory network (LSTM) with a few hidden layers and relatively short training duration. We show that the network is able to predict audio energy features of free improvisations on the guitar, relying on a dataset of three distinct motion types.This work was partially supported by the Research Council of Norway (# 262762) and NordForsk (# 86892).application/pdfen-AU© 2020 Cagrı Erdem et al.https://creativecommons.org/licenses/by/3.0/20202022-05-08Creative Commons Attribution 3.0 Unported License