标题： 波束成形的360°声音图：U-Net驱动的声源分割与定位 显示英文标题

标题： Beamformed 360° Sound Maps: U-Net-Driven Acoustic Source Segmentation and Localization

摘要： 我们引入了一个U-net模型用于360{\deg }声源定位，该模型被表述为球面语义分割任务。 而不是回归离散的到达方向（DoA）角度，我们的模型将波束成形音频图（方位角和仰角）分割成存在活跃声音的区域。 使用自定义的24麦克风阵列进行延迟与求和（DAS）波束成形，我们生成与无人机GPS遥测对齐的信号以创建二进制监督掩码。 一个修改后的U-Net，在这些图的频域表示上进行训练，能够识别空间分布的声源区域，并通过Tversky损失处理类别不平衡问题。 由于网络在波束成形能量图上运行，该方法本质上与阵列无关，可以在不从头开始重新训练的情况下适应不同的麦克风配置。 通过在激活区域上计算质心对分割输出进行后处理，从而实现稳健的DoA估计。 我们的数据集包括DJI Air 3无人机在多个日期和地点的真实开放场地录音，同步了360{\deg }视频和飞行日志。 实验结果表明，U-net在不同环境中具有泛化能力，提供了改进的角度精度，为超越传统声源定位（SSL）的密集空间音频理解提供了一种新范式。 显示英文摘要

摘要： We introduce a U-net model for 360{\deg} acoustic source localization formulated as a spherical semantic segmentation task. Rather than regressing discrete direction-of-arrival (DoA) angles, our model segments beamformed audio maps (azimuth and elevation) into regions of active sound presence. Using delay-and-sum (DAS) beamforming on a custom 24-microphone array, we generate signals aligned with drone GPS telemetry to create binary supervision masks. A modified U-Net, trained on frequency-domain representations of these maps, learns to identify spatially distributed source regions while addressing class imbalance via the Tversky loss. Because the network operates on beamformed energy maps, the approach is inherently array-independent and can adapt to different microphone configurations without retraining from scratch. The segmentation outputs are post-processed by computing centroids over activated regions, enabling robust DoA estimates. Our dataset includes real-world open-field recordings of a DJI Air 3 drone, synchronized with 360{\deg} video and flight logs across multiple dates and locations. Experimental results show that U-net generalizes across environments, providing improved angular precision, offering a new paradigm for dense spatial audio understanding beyond traditional Sound Source Localization (SSL).