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Multi-agent Auditory Scene Analysis: Improved Localization Speed and Robustness by Multi-beamformed Speech Quality Feedback — arXiv:2610.00538

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.00538
License (as archived): CC0-1.0
Archived: 2026-10-06T11:54:34.318Z
SHA-256 of the archived text: 7ef1f6172f4d99f26947db73b520d7acb73b019d0351264c31858c055eae5a17
Stable URL of this page: https://metraipolis.com/documento/dc_d8b057cdbebe

How to cite

Multi-agent Auditory Scene Analysis: Improved Localization Speed and Robustness by Multi-beamformed Speech Quality Feedback — arXiv:2610.00538 — arXiv (en), https://arxiv.org/abs/2610.00538 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d8b057cdbebe (Metraipolis archive dc_d8b057cdbebe, sha256 7ef1f6172f4d99f26947db73b520d7acb73b019d0351264c31858c055eae5a17)

BibTeX

@misc{dcd8b057cdbebe,
  title = {Multi-agent Auditory Scene Analysis: Improved Localization Speed and Robustness by Multi-beamformed Speech Quality Feedback — arXiv:2610.00538},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.00538},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d8b057cdbebe; sha256 7ef1f6172f4d99f26947db73b520d7acb73b019d0351264c31858c055eae5a17},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.00538. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A real-time auditory scene analyzer (ASA) aims to carry out the tasks of locating, separating and classifying the sound sources present in a given acoustic environment. Recently, an effort has been made into modelling an ASA as a multi-agent system, with each one of its agents performing one of the aforementioned tasks and communicating their results to the rest of their peer agents. These communication routes are used as feedback loops to fix local errors at a global level, providing robustness while reducing local complexity. An example of the benefits of this approach is the optimization of speech quality by correcting in real-time the estimated location of the speech source of interest. However, their optimization speed has been shown to be considerably slow. One possible reason is that it solely relies on a series of single quality estimations (provided by a reference-free quality estimator model) that vary considerably from one window to the next, which results in a difficult search space to optimize. In this work, a new optimization mechanism is proposed that instead relies on a series of sets of quality estimations over a range of locations, providing a clearer view of the search space, simplifying its optimization. The proposed ASA now has a considerably smaller optimization time, is more accurate, and is more stable when being evaluated in real-life acoustic scenarios to correct higher levels of localization errors, all while being less complex than previous efforts. The only trade-off is that there is an increase in the response time of the quality estimation agent, but the complete ASA is still able to run in real-time. The performance shown in this work again shows the benefits of modelling an ASA as a multi-agent system.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-02 in cs.MA).
Categorie arXiv: Audio and Speech Processing (eess.AS) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.00538 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T10:17:30.557Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).