doi:10.1109/TASLP.2022.3224282engDiaz-Guerra, DavidMiguel, AntonioBeltran, Jose R.Direction of arrival estimation of sound sources using icosahedral CNNsART-2023-131864In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a microphone array. This icosahedral CNN is equivariant to the 60 rotational symmetries of the icosahedron, which represent a good approximation of the continuous space of spherical rotations, and can be implemented using standard 2D convolutional layers, having a lower computational cost than most of the spherical CNNs. In addition, instead of using fully connected layers after the icosahedral convolutions, we propose a new soft-argmax function that can be seen as a differentiable version of the argmax function and allows us to solve the DOA estimation as a regression problem interpreting the output of the convolutional layers as a probability distribution. We prove that using models that fit the equivariances of the problem allows us to outperform other state-of-the-art models with a lower computational cost and more robustness, obtaining root mean square localization errors lower than 10∘ even in scenarios with a reverberation time T60 of 1.5s .2023http://zaguan.unizar.es/record/12123510.1109/TASLP.2022.3224282http://zaguan.unizar.es/record/121235oai:zaguan.unizar.es:121235info:eu-repo/grantAgreement/ES/DGA-FEDER/2014-2020IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (2023), 313-321All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/closedAccess