摘要
本文提出了一种基于数据驱动字典和过完备稀疏表示的自适应语音增强方法。首先在训练阶段采用干净语音基于K奇异值分解(K—singular value decomposition,K-SVD)算法训练过完备字典,然后在测试阶段根据含噪语音的噪声方差自适应选择最优的阈值,采用正交匹配追踪算法对含噪语音信号在过完备字典上进行稀疏分解,最后利用系数稀疏表示重构语音信号,从而达到语音增强的目。该方法不像传统语音增强方法那样减少或消去噪声,而是从字典中选取适当的原子表示纯净信号,从而把纯净信号从含噪信号中分离出来。对白噪声和有色噪声环境下重构语音进行了主客观评价。仿真结果显示:该方法能有效去除加性噪声,并且改善了语音质量。
An adaptive speech enhancement method based on Data-Driven Dictionary and overcompletely sparse representation theory is proposed.Firstly,using the K-singular value decomposition（K-SVD） algorithm,a dictionary that describes the clean speech content effectively is trained.Secondly,the prime threshold is adaptively selected according to noise variance of original noisy speech signal and the speech signal＇s sparsest coefficient vector is obtained through Orthogonal Matching Pursuit algorithm.And then the speech signal is recovered and speech enhancement is achieved.Different from the conventional techniques which improve the speech signal quality by suppression of noise and reduction of distortion,we select the appropriate atoms to represent speech signal.Thus, clean signal is separated from the noisy speech signal.In white or colored noise interference,the reconstructed speech signal via the proposed algorithm is evaluated by the objective and subjective evaluation.The experimental results show that the proposed algorithm can get ride of the addictive noise and improve speech quality.
出处
《信号处理》
CSCD
北大核心
2011年第12期1793-1800,共8页
Journal of Signal Processing
基金
国家重大基础研究973课题(2011CB302903)
国家自然科学基金项目(60971129)
江苏省普通高校研究生科研创新计划项目(CX10B_191Z,CX10B_189Z)
关键词
语音增强
稀疏表示
过完备字典
正交匹配追踪
奇异值分解算法
speech enhancement
sparse representation
overcomplete dictionary
Orthogonal Matching Pursuit
singular value decomposition algorithm
