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- W4226059547 abstract "In 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{deg} even in scenarios with a reverberation time $T_{60}$ of 1.5 s." @default.
- W4226059547 created "2022-05-05" @default.
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- W4226059547 date "2022-03-31" @default.
- W4226059547 modified "2023-09-24" @default.
- W4226059547 title "Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs" @default.
- W4226059547 doi "https://doi.org/10.48550/arxiv.2203.16940" @default.
- W4226059547 hasPublicationYear "2022" @default.
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