  Speech enhancement algorithms based on deep learning have been improved in
terms of speech intelligibility and perceptual quality greatly. Many methods
focus on enhancing the amplitude spectrum while reconstructing speech using the
mixture phase. Since the clean phase is very important and difficult to
predict, the performance of these methods will be limited. Some researchers
attempted to estimate the phase spectrum directly or indirectly, but the effect
is not ideal. Recently, some studies proposed the complex-valued model and
achieved state-of-the-art performance, such as deep complex convolution
recurrent network (DCCRN). However, the computation of the model is huge. To
reduce the complexity and further improve the performance, we propose a novel
method using discrete cosine transform as the input in this paper, called deep
cosine transform convolutional recurrent network (DCTCRN). Experimental results
show that DCTCRN achieves state-of-the-art performance both on objective and
subjective metrics. Compared with noisy mixtures, the mean opinion score (MOS)
increased by 0.46 (2.86 to 3.32) absolute processed by the proposed model with
only 2.86M parameters.

Audio and Speech Processing;Sound