弱引导自回归波束形成器参数化方法，用于高阶高保真立体声声场中可泛化的移动说话人提取
Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics
AI总结：
针对多未知方向移动说话人场景下波束形成器性能下降问题，提出仅需目标初始方向估计的数据驱动波束形成流水线，实现可泛化的说话人提取。
摘要：
线性空间滤波器（波束形成器）可在理想参数化条件下实现鲁棒、可泛化且可解释的语音增强，并具备性能保证。现代波束形成器通常由深度神经网络进行参数化，在存在多个方向未知的移动说话人的动态场景中，其性能会出现下降。本文提出一种数据驱动的波束形成流水线，仅需要目标说话人初始方向的估计值。基于高阶高保真立体声声场表示，证明神经时频处理可与线性空间处理解耦，进而实现可泛化、与阵列无关的增强。通过将自回归机制引入逐帧因果框架，在说话人快速移动和长时长录音场景下均能保持稳定性能。合成数据评估表明，该方法在说话人间距近、轨迹交叉的挑战性条件下可实现鲁棒增强。动态办公室会议场景的真实录音实验进一步验证了该方法在不同高保真立体声声场阶数下的泛化能力。
英文摘要：
Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization. Modern beamformers are often parameterized by deep neural networks, whose performance degrades in dynamic scenarios with multiple moving speakers of unknown directions. We propose a data-driven beamforming pipeline, which only requires an estimate of the target's initial direction. Building on a higher-order ambisonics representation, we show that neural temporal-spectral processing can be decoupled from linear spatial processing, and thereby achieve generalizable and array-agnostic enhancement. By incorporating autoregression into a frame-wise causal framework, we maintain consistent performance throughout fast speaker motion and long recordings. Evaluation on synthetic data demonstrates robust enhancement under challenging conditions with closely spaced and crossing speakers. Real-world recordings in a dynamic office meeting scenario complement these findings and show generalizability across varying ambisonics orders.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
