发表机构
Soochow University（苏州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GAMF框架利用阵列传递函数匹配，通过学习和解析分支结合，实现跨阵列几何的到达方向估计，在模拟和真实数据上优于现有基线。
AI 中文摘要
麦克风位置编码支持跨阵列的到达方向（DOA）估计，但仅凭坐标无法完全描述设备遮挡或麦克风指向性。我们提出了一种通用阵列传递函数匹配框架（GAMF），用于跨阵列几何形状和麦克风数量的DOA估计，使用阵列传递函数（ATF）作为声学描述符。学习分支将ATF嵌入融入几何条件神经估计中，以将声学观测与候选方向匹配。解析分支执行从广义转向响应功率改编的归一化ATF匹配。混合配置通过自适应门控结合两者的得分。跨阵列配置的模拟表明，学习配置和混合配置均优于基于位置编码的代表性神经基线，在干净、低混响场景中与解析ATF匹配保持竞争力，并在较强噪声或混响下显著优于后者。在八麦克风LOCATA任务1录音中，混合配置在三维DOA估计上优于评估的最先进基线，三维DOA平均误差为3.76°，方位角平均误差为2.87°。
英文摘要
Microphone positional encoding supports cross-array direction-of-arrival (DOA) estimation, but coordinates alone cannot fully describe device shadowing or microphone directivity. We propose a Generalizable ATF Matching Framework (GAMF) for DOA estimation across array geometries and microphone counts, using array transfer functions (ATFs) as acoustic descriptors. The learned branch incorporates ATF embeddings into geometry-conditioned neural estimation to match acoustic observations with candidate directions. The analytical branch performs normalized ATF matching adapted from generalized steered response power. A hybrid configuration combines their scores through adaptive gating. Simulations across array configurations show that both learned and hybrid configurations outperform a representative positional-encoding-based neural baseline, remain competitive with analytical ATF matching in clean, low-reverberation scenes, and substantially improve upon it under stronger noise or reverberation. On eight-microphone LOCATA Task 1 recordings, the hybrid configuration outperforms the evaluated state-of-the-art baselines for three-dimensional DOA estimation, achieving mean errors of $3.76^\circ$ for three-dimensional DOA and $2.87^\circ$ for azimuth.
