Adaptive Sound Localization for Robotic Platforms Using Biomimetic Deep Learning and Stochastic Confidence Mechanism

Adaptive Sound Localization for Robotic Platforms Using Biomimetic Deep Learning and Stochastic Confidence Mechanism | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 October 2025 V1 Latest version Share on Adaptive Sound Localization for Robotic Platforms Using Biomimetic Deep Learning and Stochastic Confidence Mechanism Authors : Florian Huillet 0009-0008-2011-6998 [email protected] and Tariq Zioud 0000-0001-7939-1216 Authors Info & Affiliations https://doi.org/10.22541/au.175978294.45631962/v1 323 views 147 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Sound source localization is fundamental for autonomous robotic platforms requiring spatial awareness and natural environmental interaction. This paper introduces a biomimetic two-step deep learning framework inspired by feline head-tilting behavior for precise 3D sound localization. Our approach employs a stochastic confidence mechanism that selectively triggers additional acoustic measurements, mimicking the natural decision-making process observed in biological auditory systems. Unlike traditional fixed-threshold approaches that suffer from systematic gaming behavior, our probabilistic decision framework maintains authentic biomimetic patterns while optimizing computational efficiency. The system uses a modified ResNet18 architecture with cross-modal feature fusion to process binaural acoustic cues from multiple microphone positions, predicting spherical coordinates (distance, azimuth, altitude) with uncertainty estimation. Experimental validation demonstrates robust performance with 0.74 m mean localization error and 75% predictions within 1.0 m accuracy. The stochastic mechanism achieves 79% reduction in second-stage processing (21% second-stage usage) while preserving natural headtilting frequency patterns. Real-time performance validation on NVIDIA Jetson Orin NX embedded hardware confirms practical deployment viability with inference times of 33-42 ms across different power configurations. This biomimetic approach enables more natural human-robot interaction while maintaining computational efficiency essential for autonomous robotic operations. Supplementary Material File (sound_localization_paper.pdf) Download 7.62 MB Information & Authors Information Version history V1 Version 1 06 October 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords biomimetic robotics computing and processing confidence estimation deep learning energy efficiency mobile robotics robotics and control systems signal processing and analysis sound source localization stochastic decision making Authors Affiliations Florian Huillet 0009-0008-2011-6998 [email protected] Expleo Group, ESIEA View all articles by this author Tariq Zioud 0000-0001-7939-1216 View all articles by this author Metrics & Citations Metrics Article Usage 323 views 147 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Florian Huillet, Tariq Zioud. Adaptive Sound Localization for Robotic Platforms Using Biomimetic Deep Learning and Stochastic Confidence Mechanism. Authorea . 06 October 2025. DOI: https://doi.org/10.22541/au.175978294.45631962/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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