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。

基于注意力的移动声源场景下的多通道语音增强

Attention-Driven Multichannel Speech Enhancement in Moving Sound Source  Scenarios

In multichannel speech enhancement, both spectral and spatial information are
vital for discriminating between speech and noise. How to fully exploit these
two types of information and their temporal dynamics remains an interesting
research problem. As a solution to this problem, this paper proposes a
multi-cue fusion network named McNet, which cascades four modules to
respectively exploit the full-band spatial, narrow-band spatial, sub-band
spectral, and full-band spectral information. Experiments show that each module
in the proposed network has its unique contribution and, as a whole, notably
outperforms other state-of-the-art methods.

本文提出了一种名为 McNet 的多提示融合网络，该网络级联四个模块分别利用全频带空间、窄带空间、子带谱和全频带谱信息，通过实验证明该网络的每个模块都具有其独特的贡献，整体而言显著优于其他最先进的方法。