  Multi-channel speech enhancement extracts speech using multiple microphones
that capture spatial cues. Effectively utilizing directional information is key
for multi-channel enhancement. Deep learning shows great potential on
multi-channel speech enhancement and often takes short-time Fourier Transform
(STFT) as inputs directly. To fully leverage the spatial information, we
introduce a method using spherical harmonics transform (SHT) coefficients as
auxiliary model inputs. These coefficients concisely represent spatial
distributions. Specifically, our model has two encoders, one for the STFT and
another for the SHT. By fusing both encoders in the decoder to estimate the
enhanced STFT, we effectively incorporate spatial context. Evaluations on TIMIT
under varying noise and reverberation show our model outperforms established
benchmarks. Remarkably, this is achieved with fewer computations and
parameters. By leveraging spherical harmonics to incorporate directional cues,
our model efficiently improves the performance of the multi-channel speech
enhancement.
