Modelling human sound localization with deep neural networks
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InESAN 2020: 28th European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning, pp. No. ES2020-59-1-6
ESAN 2020: 28th European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning (Bruges, Belgium, 2-4 October, 2020)
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SW OZ DCC AI
ESAN 2020: 28th European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning
p. No. ES2020-59
SubjectCognitive artificial intelligence
How the brain transforms binaural, real-life sounds into a neu-ral 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 az-imuth 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.
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