Enhance Speech with Temporal Sparse Noise by Robust Kalman Filter

摘要：

Single channel speech enhancement is an important problem in practice. One of the well used single channel speech enhancement method, spectral subtraction, can only work for stationary noise. Another method based on Kalman filtering is able to work with non-stationary signals. However, it can only produce optimal estimation of speech signal which is corrupted by Gaussian noise. In practice, speech acquisition also suffers from non-stationary temporal sparse noise. In this paper, we propose a method based on robust Kalman filter to remove not only the stationary noise but also such kind of temporal sparse noise. Formulated as a convex optimization problem, the robust Kalman filtering based method can be solved efficiently by Interior Point Method (IPM). Numerical results show that the proposed method is robust against temporal sparse noises.

作者: Zhu Liang YU Fei WU Ling CEN Zhenghui GU Yuanqing LI

作者单位: College of Automation Science and Engineering, South China University of Technology, Guangzhou Institute of InfoComn Research, A*STAR, Singapore

会议类型: 国际会议

会议名称: 2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)

会议地点: 西安

会议语种:英文

页码: 1-4

在线出版日期: 2011-10-18（万方平台首次上网日期，不代表论文的发表时间）