  Sonification, or encoding information in meaningful audio signatures, has
several advantages in augmenting or replacing traditional visualization methods
for human-in-the-loop decision-making. Standard sonification methods reported
in the literature involve either (i) using only a subset of the variables, or
(ii) first solving a learning task on the data and then mapping the output to
an audio waveform, which is utilized by the end-user to make a decision. This
paper presents a novel framework for sonifying high-dimensional data using a
complex growth transform dynamical system model where both the learning (or,
more generally, optimization) and the sonification processes are integrated
together. Our algorithm takes as input the data and optimization parameters
underlying the learning or prediction task and combines it with the
psychoacoustic parameters defined by the user. As a result, the proposed
framework outputs binaural audio signatures that not only encode some
statistical properties of the high-dimensional data but also reveal the
underlying complexity of the optimization/learning process. Along with
extensive experiments using synthetic datasets, we demonstrate the framework on
sonifying Electro-encephalogram (EEG) data with the potential for detecting
epileptic seizures in pediatric patients.
