We describe a new method for estimating the direction of sound in a
reverberant environment from basic principles of sound propagation. The method
utilizes SNR-adaptive features from time-delay and energy of the directional
components after acoustic wave decomposition of the observed sound field to
estimate the line-of-sight direction under noisy and reverberant conditions.
The effectiveness of the approach is established with real-data of different
microphone array configurations under various usage scenarios.

我们描述了一种从声音传播的基本原理来估计混响环境中声音方向的新方法。该方法利用了观察到的声场经声波分解后的时延和方向性分量的能量的信噪比自适应特征，在嘈杂和混响的条件下估计视线方向。通过在不同麦克风阵列配置和各种使用场景下的真实数据验证了该方法的有效性。

混响噪声环境中声音方向的极大似然估计

Maximum Likelihood Estimation of the Direction of Sound In A Reverberant  Noisy Environment

Current multichannel speech enhancement algorithms typically assume a
stationary sound source, a common mismatch with reality that limits their
performance in real-world scenarios. This paper focuses on attention-driven
spatial filtering techniques designed for dynamic settings. Specifically, we
study the application of linear and nonlinear attention-based methods for
estimating time-varying spatial covariance matrices used to design the filters.
We also investigate the direct estimation of spatial filters by attention-based
methods without explicitly estimating spatial statistics. The clean speech
clips from WSJ0 are employed for simulating speech signals of moving speakers
in a reverberant environment. The experimental dataset is built by mixing the
simulated speech signals with multichannel real noise from CHiME-3. Evaluation
results show that the attention-driven approaches are robust and consistently
outperform conventional spatial filtering approaches in both static and dynamic
sound environments.

当前的多通道语音增强算法通常假设声源是固定的，这与现实不符，限制了它们在实际场景中的性能。本文聚焦于为动态环境设计的基于注意力的空间滤波技术。具体地，我们研究了线性和非线性注意力方法在估计时变空间协方差矩阵以设计滤波器时的应用。我们还研究了通过注意力方法直接估计空间滤波器，而无需明确估计空间统计数据。使用 WSJ0 中的干净语音片段模拟在混响环境中移动发声者的语音信号生成实验数据集，其中混合了 CHiME-3 中的多通道真实噪声。评估结果表明，基于注意力的方法在静态和动态声音环境中均表现出鲁棒性，并始终优于传统的空间滤波方法。