Binaural sound source localization using a hybrid time and frequency domain model

Fuente: arXiv
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Auteurs principaux: Geva, Gil, Warusfel, Olivier, Dubnov, Shlomo, Dubnov, Tammuz, Amedi, Amir, Hel-Or, Yacov
Format: Preprint
Publié: 2024
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author Geva, Gil
Warusfel, Olivier
Dubnov, Shlomo
Dubnov, Tammuz
Amedi, Amir
Hel-Or, Yacov
author_facet Geva, Gil
Warusfel, Olivier
Dubnov, Shlomo
Dubnov, Tammuz
Amedi, Amir
Hel-Or, Yacov
contents This paper introduces a new approach to sound source localization using head-related transfer function (HRTF) characteristics, which enable precise full-sphere localization from raw data. While previous research focused primarily on using extensive microphone arrays in the frontal plane, this arrangement often encountered limitations in accuracy and robustness when dealing with smaller microphone arrays. Our model proposes using both time and frequency domain for sound source localization while utilizing Deep Learning (DL) approach. The performance of our proposed model, surpasses the current state-of-the-art results. Specifically, it boasts an average angular error of $0.24 degrees and an average Euclidean distance of 0.01 meters, while the known state-of-the-art gives average angular error of 19.07 degrees and average Euclidean distance of 1.08 meters. This level of accuracy is of paramount importance for a wide range of applications, including robotics, virtual reality, and aiding individuals with cochlear implants (CI).
format Preprint
id arxiv_https___arxiv_org_abs_2402_03867
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Binaural sound source localization using a hybrid time and frequency domain model
Geva, Gil
Warusfel, Olivier
Dubnov, Shlomo
Dubnov, Tammuz
Amedi, Amir
Hel-Or, Yacov
Sound
Audio and Speech Processing
This paper introduces a new approach to sound source localization using head-related transfer function (HRTF) characteristics, which enable precise full-sphere localization from raw data. While previous research focused primarily on using extensive microphone arrays in the frontal plane, this arrangement often encountered limitations in accuracy and robustness when dealing with smaller microphone arrays. Our model proposes using both time and frequency domain for sound source localization while utilizing Deep Learning (DL) approach. The performance of our proposed model, surpasses the current state-of-the-art results. Specifically, it boasts an average angular error of $0.24 degrees and an average Euclidean distance of 0.01 meters, while the known state-of-the-art gives average angular error of 19.07 degrees and average Euclidean distance of 1.08 meters. This level of accuracy is of paramount importance for a wide range of applications, including robotics, virtual reality, and aiding individuals with cochlear implants (CI).
title Binaural sound source localization using a hybrid time and frequency domain model
topic Sound
Audio and Speech Processing
url https://arxiv.org/abs/2402.03867