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The paper presents a real‑time auditory scene analyzer (ASA) modeled as a multi‑agent system, where each agent handles locating, separating, or classifying sound sources and shares results to correct local errors globally. By replacing single‑window quality estimates with sets of estimations across a range of locations, the authors achieve faster, more accurate, and more stable optimization of speech‑source localization, even in real‑life acoustic scenarios. The new mechanism reduces optimization time and complexity, though it slightly increases the response time of the quality‑estimation agent, while the overall ASA remains real‑time capable.
The paper evaluates how large language models, when integrated into a harness that provides information retrieval and software execution, perform as agents in acoustic engineering tasks. In one experiment, the agent accurately detects and locates faults in vehicle interior audio recordings, showing that a weak model limits performance regardless of harness design. In a second case study, the agent reliably designs metamaterial unit cells to meet specified insertion loss targets across a frequency range, illustrating the agent’s practical utility in acoustic design.
The paper evaluates how large language models, when integrated into a harness that supplies information and executes software, perform as agents in acoustic engineering tasks. In one experiment the agent accurately detects and locates faults in vehicle interior audio recordings, showing that a weak model limits performance regardless of harness design. In a second case study the agent reliably designs metamaterial unit cells to meet specified insertion loss criteria, illustrating how different harness components influence agent behavior.
arXiv:2610.16942v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are fast becoming a general interface to sound, yet how far their broad pretraining carries into specialized doma...
arXiv:2609.24812v1 Announce Type: new Abstract: Voice provides a natural and immediate interface for AI agents. Many settings in which voice agents could be useful, including meetings, households, an...
arXiv:2605.13841v3 Announce Type: replace-cross Abstract: Voice agents are increasingly deployed across enterprise applications. However, no existing benchmark jointly addresses realistic conversatio...