厦门大学学报（自然科学版）2017，Vol.56Issue(3)：423-428,6.DOI:10.6043/j.issn.0438-0479.201702026
一种基于非参数贝叶斯理论的语音增强算法
Speech Enhancement Based on Nonparametric Bayesian Method
吴佳雯 1刘沁婷 1曾德炉 2丁兴号 1李琳1
作者信息
- 1. 厦门大学信息科学与技术学院,福建厦门361005
- 2. 华南理工大学数学学院,广东广州510641
- 折叠
摘要
Abstract
A new speech enhancement strategy is proposed by utilizing a nonparametric Bayesian method with Spike-Slab priori (NBSP).As a sparse representation framework,the dictionary learning,sparse coefficients representation and noise variance estimation are replaced by a single procedure of Bayesian posterior estimation.First,the noisy speech is divided into clean speech,Gaussian noise and rest noise.Then,each part is modeled with a certain priori distribution.Finally,upon the adoption of Markov Chain Monte Carlo sampling algorithm,the posterior distribution can be obtained,as the clean speech and all other parameters.Without knowing the noise variance,NBSPcould be performed directly on the noisy speech to infer the sparsity of the speech.Experiments were executed on NOIZEUS database.Experiments are executed on noisy speeches from NOIZEUS database with SNR ranging from 0 dB to 10 dB,which contain three types of noise (white,train and street).And the subjective and objective measures like PESQ score and the output SegSNR are implemented to evaluate the performance of NBSP and the other state-of-the-art methods.Corresponding results show that NBSP achieves better performances,especially in conditions of non-stationary noise with low input SNR.关键词
稀疏表示/非参数贝叶斯/Spike-Slab先验/自适应字典/语音增强Key words
sparse representation/nonparametric Bayesian estimation/Spike-Slab priori/dictionary learning/speech enhancement分类
信息技术与安全科学引用本文复制引用
吴佳雯,刘沁婷,曾德炉,丁兴号,李琳..一种基于非参数贝叶斯理论的语音增强算法[J].厦门大学学报（自然科学版）,2017,56(3):423-428,6.
