Direction of arrival estimation of sound sources using icosahedral CNNs

Diaz-Guerra, David ; Miguel, Antonio (Universidad de Zaragoza) ; Beltran, Jose R. (Universidad de Zaragoza)

Resumen: 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∘ even in scenarios with a reverberation time T60 of 1.5s .

Idioma: Inglés

DOI: 10.1109/TASLP.2022.3224282

Año: 2023

Publicado en: IEEE/ACM Transactions on Audio, Speech, and Language Processing 31 (2023), 313-321
ISSN: 2329-9290

Factor impacto JCR: 4.1 (2023)

Categ. JCR: ACOUSTICS rank: 4 / 40 = 0.1 (2023) - Q1 - T1
Categ. JCR: ENGINEERING, ELECTRICAL & ELECTRONIC rank: 94 / 353 = 0.266 (2023) - Q2 - T1

Factor impacto CITESCORE: 11.3 - Computer Science (miscellaneous) (Q1) - Electrical and Electronic Engineering (Q1) - Acoustics and Ultrasonics (Q1) - Computational Mathematics (Q1)

Factor impacto SCIMAGO: 1.542 - Acoustics and Ultrasonics (Q1) - Electrical and Electronic Engineering (Q1) - Computational Mathematics (Q1) - Speech and Hearing (Q1) - Instrumentation (Q1) - Media Technology (Q1) - Signal Processing (Q1) - Computer Science (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/2014-2020

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Tecnología Electrónica (Dpto. Ingeniería Electrón.Com.)
Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)