摘要
在鲁棒主成分分析中引入非负矩阵分解学习得到非负语音字典,提出了一种非负字典训练和鲁棒主成分分析相结合的非监督单通道语音增强算法.算法采用交替方向乘子计算优化解.采用客观感知语音质量评估方法(PESQ)对增强效果进行评估.评估中采用了TIMIT标准语音和20多种噪声在不同信噪比下进行混合得到的带噪语音信号.评估结果表明:本文提出的方法优于典型的非负矩阵分解方法和鲁棒主成分方法.
An unsupervised single channel speech enhancement algorithm is proposed.It combines both the nonnegative dictionary training and Robust Principal Component Analysis(RPCA) so that we name it as NRPCA in short.The combination is accomplished by incorporating the nonnegative speech dictionary into the RPCA model,which can be learned via Nonnegative Matrix Factorization(NMF).With the NRPCA model,the method of Alternating Direction Method of Multipliers(ADMM) is applied for optimized solutions.Objective evaluations using Perceptual Evaluation of Speech Quality(PESQ) on TIMIT with 20 noise types at various Signal-to-Noise Ratio(SNR) levels demonstrate that the proposed NRPCA model yields superior results over the conventional NMF and RPCA methods.
作者
任郑兵
倪永婧
石佳佳
邹霞
REN Zhengbing;NI Yongjing;SHI Jiajia;ZOU Xia(First Military Representative Office of Air Force Equipment Department in Changsha,Changsha 410100,China;College of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang 050000,China;College of Information Science and Engineering,Yanshan University,Qinhuangdao 066000,China;Shanghai Nanhui Senior High School,Shanghai 201300,China;Institute of Command and ControlEngineering,Arm Engineering University,Nanjing 210007,China)
出处
《复旦学报（自然科学版）》
CAS
CSCD
北大核心
2019年第3期363-369,377,共8页
Journal of Fudan University：Natural Science
关键词
语音增强
鲁棒主成分分析
非负字典训练
speech enhancement
robust principal component analysis
nonnegative dictionary training
