  Most deep learning-based models for speech enhancement have mainly focused on
estimating the magnitude of spectrogram while reusing the phase from noisy
speech for reconstruction. This is due to the difficulty of estimating the
phase of clean speech. To improve speech enhancement performance, we tackle the
phase estimation problem in three ways. First, we propose Deep Complex U-Net,
an advanced U-Net structured model incorporating well-defined complex-valued
building blocks to deal with complex-valued spectrograms. Second, we propose a
polar coordinate-wise complex-valued masking method to reflect the distribution
of complex ideal ratio masks. Third, we define a novel loss function, weighted
source-to-distortion ratio (wSDR) loss, which is designed to directly correlate
with a quantitative evaluation measure. Our model was evaluated on a mixture of
the Voice Bank corpus and DEMAND database, which has been widely used by many
deep learning models for speech enhancement. Ablation experiments were
conducted on the mixed dataset showing that all three proposed approaches are
empirically valid. Experimental results show that the proposed method achieves
state-of-the-art performance in all metrics, outperforming previous approaches
by a large margin.
