Modelling human sound localization with deep neural networks

How the brain transforms binaural, real-life sounds into a neural representation of sound location is unclear. This paper introduces a deep learning approach to address these neurocomputational mechanisms: We develop a biological-inspired deep neural network model of sound azimuth encoding operating on auditory nerve representations of real-life sounds. We explore two types of loss functions: Euclidean distance and angular distance. Our results show that a network resembling the early stages of the human auditory pathway can predict sound azimuth location. The type of loss function modulates spatial acuity in different ways. Finally, learning is independent of environment-specific acoustic properties.