Complex Spectral Mapping with a Convolutional Recurrent Network for Monaural Speech Enhancement

Phase is important for perceptual quality in speech enhancement. However, it seems intractable to directly estimate phase spectrogram through supervised learning due to lack of clear structure in phase spectrogram. Complex spectral mapping aims to estimate the real and imaginary spectrograms of clean speech from those of noisy speech, which simultaneously enhances magnitude and phase responses of noisy speech. In this paper, we propose a new convolutional recurrent network (CRN) for complex spectral mapping, which leads to a causal system for noise- and speaker-independent speech enhancement. In terms of objective intelligibility and perceptual quality, the proposed CRN significantly outperforms an existing convolutional neural network (CNN) for complex spectral mapping, as well as a strong CRN for magnitude spectral mapping. We additionally incorporate a newly-developed group strategy to substantially reduce the number of trainable parameters and the computational cost without sacrificing performance.