Ricerc@Sapienza

The performance of hypercomplex adaptive filters has been widely experimented during the last decade. Quaternion filters, especially, have been utilized in systems where the signals to be processed have some form of correlation. However, besides correlation, some resolved explanation about what particular algebra to use in a certain context has not been provided, yet. This work tries to contribute in this direction by proposing an experiment that puts filters to the test with changing the input signal geometry.

Learning from data in the quaternion domain enables us to exploit internal dependencies of 4D signals and treating them as a single entity. One of the models that perfectly suits with quaternion-valued data processing is represented by 3D acoustic signals in their spherical harmonics decomposition. In this paper, we address the problem of localizing and detecting sound events in the spatial sound field by using quaternion-valued data processing.