  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.
