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.
By Arne Hildenbrand, Philipp Diel, Zhuo Cao, Hanno Scharr, Stefan Pischinger, Steffen Marburg
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 ma...
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.
By Caleb Rascon
arXiv:2606. 15141v1 Announce Type: cross Abstract: While LALMs show promise on audio question answering, they fail to focus on question-relevant segments of audio and provide a clear, checkable reasoning process when dealing with complex audio reasoning.
By Siyuan Zhang, Jian Zong, Junyu Wang, Peiyuan Jiang, Jiahao Yan, Jingyu Zhang, Tianrui Wang, Xiaobao Wang, Longbiao Wang, Jianwu Dang
arXiv:2610.27670v1 Announce Type: new
Abstract: This paper introduces a novel framework for efficiently evaluating in-game sound designs using multimodal learning and reinforcement learning. Sound de...
By Thai Nguyen Van, Ibrahim Khan, Mury Fajar Dewantoro, Pratch Suntichaikul, Hai Van Pham, Ruck Thawonmas
AbsorbEvo is an agentic framework that autonomously designs microwave absorbers by translating natural‑language performance goals into full‑wave‑simulated designs. It combines language reasoning, physics‑based prediction, and historical feedback to guide a candidate evolution strategy, using a low‑cost predictive model to rank designs before simulation. In tests on AbsorbBench‑36, AbsorbEvo achieved a 79.17% task success rate, outperforming generic agents and random search.
By Zhicheng Feng, Yubo Zhao, Xuefeng Yao