  With the rapid development of neural networks in recent years, the ability of
various networks to enhance the magnitude spectrum of noisy speech in the
single-channel speech enhancement domain has become exceptionally outstanding.
However, enhancing the phase spectrum using neural networks is often
ineffective, which remains a challenging problem. In this paper, we found that
the human ear cannot sensitively perceive the difference between a precise
phase spectrum and a biased phase (BP) spectrum. Therefore, we propose an
optimization method of phase reconstruction, allowing freedom on the
global-phase bias instead of reconstructing the precise phase spectrum. We
applied it to a Conformer-based Metric Generative Adversarial Networks (CMGAN)
baseline model, which relaxes the existing constraints of precise phase and
gives the neural network a broader learning space. Results show that this
method achieves a new state-of-the-art performance without incurring additional
computational overhead.
