Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs
| Autor: | Diaz-Guerra, David, Miguel, Antonio, Beltran, Jose R. |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 313-321, 2023 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/TASLP.2022.3224282 |
| Popis: | 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. Comment: The code to reproduce this work can be found in our GitHub repository: https://github.com/DavidDiazGuerra/icoDOA |
| Databáze: | arXiv |
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