Proceedings of the 17th Sound and Music Computing Conference. 2020, 177-184
In 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 instrument' designs. 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.