A machine learning toolbox for musician computer interaction
Abstract:
This paper presents the SARC EyesWeb Catalog, (SEC), a machine learning toolbox that has been specifically developed 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 regression 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 generalisation error, as opposed to the inter-personal generalisation error that is more common in other areas of human-computer interaction.
Recorded at: 11th International Conference on New Interfaces for Musical Expression. 30 May – 1 June 2011, Oslo, Norway. nime2011.org
The paper: A machine learning toolbox for musician computer interaction
The software: SARC EyesWeb Catalog [SEC]
Although written in the context of musician-computer interaction, the techniques described here could just as easily be applied to exploration or authoring of a topic map. Or for that matter exploring a data stream that is being presented to a user.
Imagine that one hand gives “focus” to some particular piece of data and the other hand “overlays” a query onto that data that then displays a portion of a topic map with that data as the organizing subject. Based on that result the data can be simply dumped back into the data stream or “saved” for further review and analysis.