Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.
This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction.
College of Mathematics and Computer Science, Fuzhou University, China. Electronic address: ysxia@fzu.edu.cn.
Youshen
Y
Xia
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong. Electronic address: jwang@mae.cuhk.edu.hk.
Jun
J
Wang
D000465:Algorithms; D003143:Communication Devices for People with Disabilities; D006801:Humans; D016018:Least-Squares Analysis; D016571:Neural Networks, Computer; D013060:Speech
Noise-constrained estimation; Non-Gaussian noise; Recurrent neural network; Speech enhancement