A Hybrid Deep-Learning Approach for Single Channel HF-SSB Speech Enhancement

Abstract

The high-frequency single side-band (HF-SSB) speech radio is an essential technique for long-distance speech transmission. However, the HF-SSB received speech is corrupted by both high-power noise and severe channel fading, and the typical speech enhancement methods only focus on the suppression of additive noise. In this letter, a two-stage hybrid approach is proposed to enhance the speech quality of the HF-SSB radio. In the anti-fading stage, we adopt the anti-fading convolution neural network (AF-CNN) to eliminate the effects of channel fading. In the noise suppression stage, noise suppression CNN (NS-CNN) subnet and unsupervised denoising block are used parallelly to further improve the performance and the generalization ability. In addition, we present the optimal topological relations of noise suppression and anti-fading modules by comparison and analysis. Experimental results show that applying the AF-CNN subnet before noise suppression can effectively help recover the weak speech components. Moreover, in terms of objective intelligibility and quality scores, the overall performance of the proposed method outperforms the typical methods that only consider noise suppression.