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Sonification, or encoding information in meaningful audio signatures, hasseveral advantages in augmenting or replacing traditional visualization methodsfor human-in-the-loop decision-making. Standard sonification methods reportedin 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 toan audio waveform, which is utilized by the end-user to make a decision. Thispaper presents a novel framework for sonifying high-dimensional data using acomplex growth transform dynamical system model where both the learning (or,more generally, optimization) and the sonification processes are integratedtogether. Our algorithm takes as input the data and optimization parametersunderlying the learning or prediction task and combines it with thepsychoacoustic parameters defined by the user. As a result, the proposedframework outputs binaural audio signatures that not only encode somestatistical properties of the high-dimensional data but also reveal theunderlying complexity of the optimization/learning process. Along withextensive experiments using synthetic datasets, we demonstrate the framework onsonifying Electro-encephalogram (EEG) data with the potential for detectingepileptic seizures in pediatric patients.

Using growth transform dynamical systems for spatio-temporal data sonification