SFTRLS-Based Speech Enhancement Method Using CNN to Determine the Noise Type and the Optimal Forgetting Factor

Abstract

This paper presents a speech enhancement method combining the convolutional neural network (CNN) and SFTRLS, SFTRLS-CNN, which consists of two tiers of CNN to customize parameters for the SFTRLS algorithm. The first CNN identifies noise type, and the second CNN matches the best forgetting factor. The experimental results show that the noise recognition rate of SFTRLS-CNN goes up to 99.97% and displays better performance than the k-nearest neighbor (KNN) and the support vector machine (SVM). The accuracy ratio of matching the best forgetting factor for the SFTRLS is up to 99.40%. The improvement of the perceptual evaluation of speech quality (PESQ) is 23%, and the decrease of log-spectral distortion (LSD) is 4% on average. SFTRLS-CNN also improves the SNR of all speeches significantly.