  Multi-channel speech enhancement utilizes spatial information from multiple
microphones to extract the target speech. However, most existing methods do not
explicitly model spatial cues, instead relying on implicit learning from
multi-channel spectra. To better leverage spatial information, we propose
explicitly incorporating spatial modeling by applying spherical harmonic
transforms (SHT) to the multi-channel input. In detail, a hierarchical
framework is introduced whereby lower order harmonics capturing broader spatial
patterns are estimated first, then combined with higher orders to recursively
predict finer spatial details. Experiments on TIMIT demonstrate the proposed
method can effectively recover target spatial patterns and achieve improved
performance over baseline models, using fewer parameters and computations.
Explicitly modeling spatial information hierarchically enables more effective
multi-channel speech enhancement.
