ILMSAF based speech enhancement with DNN and noise classification

Abstract

In order to improve the performance of speech enhancement algorithm in low Signal-to-Noise Ratio (SNR) complex noise environments, a novel Improved Least Mean Square Adaptive Filtering (ILMSAF) based speech enhancement algorithm with Deep Neural Network (DNN) and noise classification is proposed. An adaptive coefficient of filter's parameters is introduced into conventional Least Mean Square Adaptive Filtering (LMSAF). First, the adaptive coefficient of filter's parameters is estimated by Deep Belief Network (DBN). Then, the enhanced speech is obtained by ILMSAF. In addition, in order to make the presented approach suitable for various kinds of noise environments, a new noise classification method based on DNN is presented. According to the result of noise classification, the corresponding ILMSAF model is selected in the enhancement process. The performance test results under ITU-TG.160 show that, the performance of the proposed algorithm tends to achieve significant improvements in terms of various speech subjective and objective quality measures than the wiener filtering based speech enhancement approach with Weighted Denoising Auto-encoder and noise classification.