利用阵列传递函数的阵列通用神经网络到达方向估计
Neural Array-Generic Direction-of-Arrival Estimation Exploiting Array Transfer Functions
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中文总结 AI 辅助
该研究提出利用阵列传递函数的阵列通用神经网络到达方向估计框架，可泛化至未见过的麦克风阵列，在混响与噪声环境下定位性能与基线相当。
中文摘要 AI 辅助
到达方向（DoA）估计是多通道音频处理的关键组成部分，但许多深度学习方法仍与训练期间使用的麦克风阵列绑定，对未见过的设备泛化能力较差。本文提出一种阵列通用神经网络DoA估计框架，该框架使用与现实多麦克风设备匹配的实测或模拟复定向阵列传递函数（ATF）。该方法通过独立的卷积编码器处理多通道语谱图和ATF元数据，通过交叉注意力融合所得表示，并采用多源笛卡尔向量输出公式预测声源方向。在混响和扩散 babble 噪声下的模拟2D和3D定位任务实验表明，所提方法可泛化到先前未见过的阵列，包括类似手机的配置，且无显著性能下降，同时与传统及基于学习的基线相比具有竞争力。
英文摘要
Direction-of-arrival (DoA) estimation is a key component of multichannel audio processing, yet many deep learning approaches remain tied to the microphone arrays used during training and generalize poorly to unseen devices. This paper proposes an array-generic neural DoA estimation framework using measured or simulated complex directional array transfer functions (ATFs) matched to real-world multi-microphone devices. The method processes multichannel spectrograms and ATF metadata with separate convolutional encoders, fuses the resulting representations through cross-attention, and predicts source directions using a multi-source Cartesian vector output formulation. Experiments on simulated 2D and 3D localization tasks under reverberation and diffuse babble noise show that the proposed approach generalizes to previously unseen arrays, including mobile-phone-like configurations, without major performance degradation, while remaining competitive with conventional and learning-based baselines.
