  Phase information has a significant impact on speech perceptual quality and
intelligibility. However, existing speech enhancement methods encounter
limitations in explicit phase estimation due to the non-structural nature and
wrapping characteristics of the phase, leading to a bottleneck in enhanced
speech quality. To overcome the above issue, in this paper, we proposed
MP-SENet, a novel Speech Enhancement Network that explicitly enhances Magnitude
and Phase spectra in parallel. The proposed MP-SENet comprises a
Transformer-embedded encoder-decoder architecture. The encoder aims to encode
the input distorted magnitude and phase spectra into time-frequency
representations, which are further fed into time-frequency Transformers for
alternatively capturing time and frequency dependencies. The decoder comprises
a magnitude mask decoder and a phase decoder, directly enhancing magnitude and
wrapped phase spectra by incorporating a magnitude masking architecture and a
phase parallel estimation architecture, respectively. Multi-level loss
functions explicitly defined on the magnitude spectra, wrapped phase spectra,
and short-time complex spectra are adopted to jointly train the MP-SENet model.
A metric discriminator is further employed to compensate for the incomplete
correlation between these losses and human auditory perception. Experimental
results demonstrate that our proposed MP-SENet achieves state-of-the-art
performance across multiple speech enhancement tasks, including speech
denoising, dereverberation, and bandwidth extension. Compared to existing
phase-aware speech enhancement methods, it further mitigates the compensation
effect between the magnitude and phase by explicit phase estimation, elevating
the perceptual quality of enhanced speech.
