基于概率潜分量分析的语音增强算法  

Speech Enhancement Algorithm Based on Probabilistic Latent Component Analysis

作　　者：应涛[1] 黄高明[1] 周成[1] 

机构地区：[1]海军工程大学电子工程学院,湖北武汉430033

出　　处：《四川大学学报（工程科学版）》2014年第1期128-133,共6页Journal of Sichuan University (Engineering Science Edition)

基　　金：国家"863"高技术研究发展计划资助项目(2011AA7014061;2012AA7014061);国家自然科学基金资助项目(60901069)

摘　　要：针对传统语音增强方法在非平稳噪声环境和低信噪比情况下增强效果不理想的问题,提出了一种基于概率潜分量分析(PLCA)的语音增强算法。该算法分析并引入了PLCA模型,将语音谱建模成意义明晰的边缘分布表示,并通过期望最大化(EM)算法对最优边缘分布进行求解,用边缘分布组成的字典对噪声进行描述,利用语音信号的边缘分布选择性地重构语音信号,从而实现与噪声的分离,达到语音增强的目的。仿真结果表明,该算法在抑制噪声、提高信噪比、增强语音质量方面明显优于传统的语音增强方法。In order to treat the problem that the effect of traditional speech enhancement methods is not satisfactory under the condition of non-stationary noise environment and low SNR（ signal to noise ratio） , an algorithm of speech enhancement based on probabilistie la- tent component analysis was proposed. By analyzing and introducing probabilistic latent component analysis, phonic spectrogram was explicitly modeled as a mixture of marginal distribution products and noise was described by a dictionary of marginals. The estimation of the most appropriate marginal distributions was performed using expectation-maximization algorithm, which is used selectively to recon- struct the signal, separating it from noise, and the goal of speech enhancement was achieved. Simulation results demonstrated that the proposed algorithm is more effective in terms of redtlcing background noise, improving SNR and decreasing speech distortion than tradi- tional speech enhancement algorithms.

关 键 词：潜变量 概率潜分量分析 EM算法 语音增强 

分 类 号：TN912.3[电子电信—通信与信息系统]