基于增益字典查询的语音增强算法

对于基于统计模型的语音增强算法,不同分布模型对应于不同的增益函数,由于语音信号的不确定性,没有一种分布函数能准确对语音和噪声谱的分布建模,因此任何一种固定的统计模型均会存在一定的误差。所以提出一种增益字典查询的语音增强算法,该算法通过采用对数谱失真准则对一个语音噪声库进行增益的训练,得到一个增益的字典,其中输入为先验信噪比和后验信噪比的估计值。最后采用ITU-T P.826 PESQ、分段信噪比、总信噪比和对数谱失真对该算法进行了测试,并与基于高斯分布模型、拉普拉斯分布模型的算法进行了对比。实验结果表明,该算法无论在非平稳噪声还是平稳噪声环境下都比其他几种算法增强效果好,且音乐噪声和残留背景噪声也可以得到很好的抑制。

Speech Enhancement Based on Gain Dictionary Queries

For speech enhancement algorithm based on statistical model,different distribution models are corresponding to different gain function,due to the uncertainty of the speech signal,no distribution function can accurately model the speech and noise spectra distribution,so any kind of fixed reference models will have some errors.We presented a gain dictionary queries based speech enhancement algorithm,getting a dictionary gain through training the voice of a noise library using log-spectral distortion criterion,for which the input is the estimate value of a priori and a posteriori SNR.Finally,we used ITU-T P.826 PESQ,segmented SNR,total SNR and log-spectral distortion criterion to test the proposed algorithm,and compared this algorithm with Gaussian distribution model and Laplace distribution model.The experimental results show that the algorithm is better than the other algorithms,whether in stationary or non-stationary noisy environments,and musical noise and residual background noise can be well suppressed.