Neural and statistical processing of spatial cues for sound source localisation

Abstract

When confronting binaural sound source localisation (SSL) algorithms with different environments and robotic platforms, there is an increasing need for non-linear integration methods of spatial cues. Based on interaural time and level differences, we compare the performance of several SSL systems. The architecture has three degrees of freedom, i.e. each tested architecture employs a different combination of representation of binaural cues, clustering and classification algorithms. The heuristic for the selection of methods is the same at each degree of freedom: to compare the impact of traditional statistical techniques versus machine learning algorithms with different degrees of biological inspiration. The overall performance is evaluated in the analysis of each system, including the accuracy of its output, training time and adequateness for life-long learning. The results support the use of hybrid systems, consisting different kinds of artificial neural networks, as they present an effective compromise between the characteristics evaluated.