发表机构
School of Computing, National University of Singapore（新加坡国立大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对无人机听觉场景中旋翼噪声主导混合信号的问题，提出DRONEAUDIONET噪声抑制方法，引入可学习掩码缩放机制与残差校正项，经实验验证可提升下游声音分类性能，凸显其在无人机辅助搜救中的应用潜力。
AI 中文摘要
安装在无人机（UAV）上的麦克风可实现空中声学场景分析应用，如搜救、野生动物监测和工业巡检。然而，无人机旋翼噪声往往在信噪比（SNR）远低于-10 dB时主导混合信号，导致声源恢复极具挑战性。现有的增强和声源分离方法通常针对近平衡混合信号设计，在无人机听觉场景中性能大幅下降。本研究提出DRONEAUDIONET，一种无人机噪声抑制方法，将声源分离模型重新定义为无人机噪声估计模型。为更好地建模无人机主导的混合信号，引入可学习的掩码缩放机制，允许掩码幅度超过1，并添加附加残差校正项以提升无人机估计和声源恢复效果。在公开的无人机听觉数据集上对模型进行训练和评估，并在包含未见过的无人机硬件和飞行模式的域外数据集上测试泛化能力。结果显示，DRONEAUDIONET持续提升下游声音分类性能，其中人声的增益最为显著。研究结果表明，无人机特定建模对实现稳健的空中声学感知至关重要，并凸显了声源分离方法在现实世界无人机辅助搜救中的应用潜力。
英文摘要
Microphones mounted on UAVs enable aerial acoustic scene analysis applications such as search-and-rescue, wildlife monitoring, and industrial inspection. However, drone rotor noise often dominates the mixture signal at SNRs well below -10 dB, making source recovery extremely challenging. Existing enhancement and source separation methods are typically designed for near-balanced mixtures and degrade substantially in drone audition settings. In this work, we propose DRONEAUDIONET, a drone noise suppression method that reframes a source separation model as a drone noise estimator. To better model drone-dominant mixtures, we introduce a learnable mask-scaling mechanism that allows mask magnitudes beyond unity, together with an additive residual correction term for improved drone estimation and source recovery. We train and evaluate our model on a publicly available drone audition dataset and test generalizability on an out-of-domain dataset with unseen drone hardware and flight modes. Results show that DRONEAUDIONET consistently improves downstream sound classification performance, with the largest gains observed for human vocal sounds. Our findings demonstrate the importance of drone-specific modeling for robust aerial acoustic perception and highlight the potential of source separation methods for real-world drone-assisted search-and-rescue.
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