DENSELY CONNECTED MULTI-STAGE MODEL WITH CHANNEL WISE SUBBAND FEATURE FOR REAL-TIME SPEECH ENHANCEMENT

Research on single channel speech enhancement (SE) has a long tradition, but two main practical problems still remain unsolved. Firstly, it's hard to balance between enhancement quality and computational efficiency, and low-latency always brings loss of quality. Secondly, enhancement in specific scenarios, such as singing and emotional speech, is also an intricate problem of conventional methods. In this paper, we propose a computationally efficient real-time speech enhancement network with densely connected multi-stage structures, which progressively enhances the channel-wise subband speech. The enhanced speech from earlier stage is used to guide the processing of deeper stage in order to obtain coarse to fine estimations. Besides, supervision is applied to all intermediate results in order to stabilize training and accelerate convergence. Moreover, an adaptive fine-tune step is utilized with some small datasets of specific scenarios, which achieves superb improvement under corresponding scenes. As a result, the proposed method achieves promising performance improvements in terms of speech quality and demonstrates robustness in complex scenarios. We submitt the proposed method to the deep noise suppression (DNS) challenge 2021, real-time denoising track, which was held by Microsoft. In the subjective evaluation, our system outperforms DNS-Challenge baseline by 0.14 points in terms of mean opinion score (MOS).