A machine learning toolbox for musician computer interaction

This paper presents the SARC EyesWeb Catalog, (SEC), a machine learning toolbox that has been specifically devel-oped for musician-computer interaction. The SEC features a large number of machine learning algorithms that can be used in real-time to recognise static postures, perform re-gression and classify multivariate temporal gestures. The algorithms within the toolbox have been designed to work with any N-dimensional signal and can be quickly trained with a small number of training examples. We also provide the motivation for the algorithms used for the recognition of musical gestures to achieve a low intra-personal gener-alisation error, as opposed to the inter-personal generalisa-tion error that is more common in other areas of human-computer interaction