Adapting general purpose interfaces to synthesis engines using unsupervised dimensionality reduction techniques and inverse mapping from features to parameters

In this paper we develop adaptive techniques for mapping generic user interfaces to synthesis engines. Upon selecting a subset of synthesis parameters, the system automatically finds the parameters-to-sound deterministic relationship in a multidimensional space. We analyze this sonic space using two different unsupervised dimensionality reduction techniques and we build the mapping using statistical information on a lower, but maximally representative, number of dimensions. The result is an adaptation of any generalpurpose interface to a specific synthesis engine, providing control directly over the perceptual features with greatest variance. This approach guarantees a linear relationship between control signals and perceptual features, and at the same time, reduces the control space dimensionality maintaining the maximum explorability of the sonic space.