Multi-agent Auditory Scene Analysis

[2507.02755] Multi-agent Auditory Scene Analysis Skip to main content Search Submit Donate Log in Electrical Engineering and Systems Science > Audio and Speech Processing arXiv:2507.02755 (eess) [Submitted on 3 Jul 2025 ( v1 ), last revised 20 Aug 2025 (this version, v3)] Title: Multi-agent Auditory Scene Analysis Authors: Caleb Rascon , Luis Gato-Diaz , Eduardo García-Alarcón View PDF HTML (experimental) Abstract: Auditory scene analysis (ASA) aims to retrieve information from the acoustic environment, by carrying out three main tasks: sound source location, separation, and classification. These tasks are traditionally executed with a linear data flow, where the sound sources are first located; then, using their location, each source is separated into its own audio stream; from each of which, information is extracted that is relevant to the application scenario (audio event detection, speaker identification, emotion classification, etc.). However, running these tasks linearly increases the overall response time, while making the last tasks (separation and classification) highly sensitive to errors of the first task (location). A considerable amount of effort and computational complexity has been employed in the state-of-the-art to develop techniques that are the least error-prone possible. However, doing so gives rise to an ASA system that is non-viable in many applications that require a small computational footprint and a low response time, such as bioacoustics, hearing-aid design, search and rescue, human-robot interaction, etc. To this effect, in this work, a multi-agent approach is proposed to carry out ASA where the tasks are run in parallel, with feedback loops between them to compensate for local errors, such as: using the quality of the separation output to correct the location error; and using the classification result to reduce the localization's sensitivity towards interferences. The result is a multi-agent auditory scene analysis (MASA) system that is robust against local errors, without a considerable increase in complexity, and with a low response time. The complete proposed MASA system is provided as a publicly available framework that uses open-source tools for sound acquisition and reproduction (JACK) and inter-agent communication (ROS2), allowing users to add their own agents. Comments: Submitted to Applied Soft Computing Subjects: Audio and Speech Processing (eess.AS) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2507.02755 [eess.AS] (or arXiv:2507.02755v3 [eess.AS] for this version) https://doi.org/10.48550/arXiv.2507.02755 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Engineering Applications of Artificial Intelligence, 2026 Related DOI : https://doi.org/10.1016/j.engappai.2026.115936 Focus to learn more DOI(s) linking to related resources Submission history From: Caleb Rascon [ view email ] [v1] Thu, 3 Jul 2025 16:16:46 UTC (737 KB) [v2] Sun, 17 Aug 2025 19:24:34 UTC (737 KB) [v3] Wed, 20 Aug 2025 14:18:03 UTC (711 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: eess.AS < prev | next > new | recent | 2025-07 Change to browse by: cs cs.AI eess References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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