Deep Learning-Based Binaural Speech Signal Enhancement Method
Cheng Wei, Zhiling Yang · 2023
The binaural speech signal enhancement decomposition structure is usually monolayered, with limited signal enhancement effect and low signal regularity. In such a context, this paper presents an analysis of the design and validation of a deep learning-based binaural speech signal enhancement method. With the method, the speech and noise features can be extracted according to the actual enhancement requirements and criteria, and signal time-frequency multi-stage decomposition and signal waveform reconstruction can be realized in the multi-stage form to break the limitation of signal enhancement effect and build a deep learning signal enhancement model. The IMCRA spectral subtraction combined processing has been adopted to realize signal enhancement. The test results show that after enhancing the selected binaural speech signals, the distortion of the speech signal enhanced could be significantly improved, with the noise side the signals eliminated, interference reduced, and signals enhanced regularly, which indicates that the enhancement method of such signal could achieve better effect in maintaining the stability of signals during the processing procedure and improving the signal frequency, and therefore is of great application value.