Speech Enhancement Based on Fusion of Both Magnitude/Phase-Aware Features and Targets

Electronics

Jie Yang¹  Haitao Lang¹ 

[1] School of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100026, China;

关键词: speech enhancement;    acoustic feature;    phase estimation;    deep neural networks (DNNs);   

DOI  :  10.3390/electronics9071125

来源: DOAJ

【 摘 要 】

Recently, supervised learning methods have shown promising performance, especially deep neural network-based (DNN) methods, in the application of single-channel speech enhancement. Generally, those approaches extract the acoustic features directly from the noisy speech to train a magnitude-aware target. In this paper, we propose to extract the acoustic features not only from the noisy speech but also from the pre-estimated speech, noise and phase separately, then fuse them into a new complementary feature for the purpose of obtaining more discriminative acoustic representation. In addition, on the basis of learning a magnitude-aware target, we also utilize the fusion feature to learn a phase-aware target, thereby further improving the accuracy of the recovered speech. We conduct extensive experiments, including performance comparison with some typical existing methods, generalization ability evaluation on unseen noise, ablation study, and subjective test by human listener, to demonstrate the feasibility and effectiveness of the proposed method. Experimental results prove that the proposed method has the ability to improve the quality and intelligibility of the reconstructed speech.

【 授权许可】

Unknown