  In this work, we present DeFTAN-II, an efficient multichannel speech
enhancement model based on transformer architecture and subgroup processing.
Despite the success of transformers in speech enhancement, they face challenges
in capturing local relations, reducing the high computational complexity, and
lowering memory usage. To address these limitations, we introduce subgroup
processing in our model, combining subgroups of locally emphasized features
with other subgroups containing original features. The subgroup processing is
implemented in several blocks of the proposed network. In the proposed split
dense blocks extracting spatial features, a pair of subgroups is sequentially
concatenated and processed by convolution layers to effectively reduce the
computational complexity and memory usage. For the F- and T-transformers
extracting temporal and spectral relations, we introduce cross-attention
between subgroups to identify relationships between locally emphasized and
non-emphasized features. The dual-path feedforward network then aggregates
attended features in terms of the gating of local features processed by dilated
convolutions. Through extensive comparisons with state-of-the-art multichannel
speech enhancement models, we demonstrate that DeFTAN-II with subgroup
processing outperforms existing methods at significantly lower computational
complexity. Moreover, we evaluate the model's generalization capability on
real-world data without fine-tuning, which further demonstrates its
effectiveness in practical scenarios.
