arXiv:2607.06299eess.AScs.SD2026-07

用物理模型模拟森林声音传播,优化麦克风阵列生物声学监测

ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing

论文配图:ForestIR: Physics-Informed Forest Sound Simulation for Array-Based Bioacoustic Remote Sensing
图 1 · 摘自论文原文
  • 基于物理规则生成受控环境下的声音传播响应
  • 可模拟不同森林布局与气象条件对定位精度的影响
  • 适合用于阵列设计验证和合成数据生成

基于麦克风阵列的被动声学监测在森林生物多样性感知中日益重要。然而,阵列系统设计与评估仍面临挑战,因实地录音成本高、难以复现,且对森林与大气条件控制有限。本文提出ForestIR,一个物理驱动且可复现的仿真框架,将森林与环境条件与麦克风阵列录音相连接,用于生物声学遥感。通过更真实的声波传播建模和对阵列设计与环境因素的系统性控制,ForestIR为优化阵列监测系统(尤其声源定位)提供了实用工具。该框架可在用户设定的森林与大气条件下生成源-麦克风脉冲响应(IR),并通过卷积测试信号与可控背景噪声,合成阵列录音。实验验证了其在森林布局与气象条件变化下定位敏感性的合理性,并对比了仿真IR与野外正弦扫频测量结果。ForestIR可有效评估森林、地面条件、大气状态及阵列几何对生物声学定位的影响,支持麦克风阵列设计、鲁棒性测试及合成数据生成。

原文摘要 · Abstract (English)

Microphone array-based passive acoustic monitoring is increasingly used for biodiversity sensing in forests. However, design and evaluation of array systems and configurations remains difficult since field recordings are costly, difficult to reproduce, and provide limited control over forest and atmospheric conditions. We present ForestIR, a physics-informed and reproducible simulation framework that links forest and environmental conditions to microphone-array recordings for bioacoustic remote sensing. Through a more realistic sound propagation method and a systematic control over array design and environmental factors, ForestIR provides a practical simulation framework for optimizing array-based monitoring systems, especially for sound source localization purposes. ForestIR generates source-microphone impulse responses (IRs) under user-controlled forest and atmospheric conditions, and renders synthetic array recordings by convolving test signals with controlled background noise. We evaluate and demonstrate realistic features of ForestIR through experiments based on localization sensitivity to forest layout and atmospheric conditions, and also comparison between simulated IRs with sine-sweep IR measurements from a field experiment. ForestIR provides a practical way to test how forest and ground conditions, atmospheric state, and array geometry affect bioacoustic localization, and can support microphone-array design, robustness testing, and synthetic-data generation for passive acoustic monitoring.

声学仿真生物声学麦克风阵列物理模型

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