改进的参数自适应的维纳滤波语音增强算法

摘要

To explore that different effects of different types of noise have on the performance of speech enhancement algorithm, a parameter adaptive Wiener filtering speech enhancement algorithm with setting different initial parameters and making the different noise power spectrum estimation according to different types of noise was proposed.The deep neural network was used to classify the noise, and the accurate classification result was obtained.For different noises, the optimally coefficient combination for the Wiener filtering algorithm integrated with the voice activity detection noise power estimator was obtained.A series of experiments were carried out.The objection evaluation shows that the proposed algorithm facing the Babble noise and 5 db SNR increases the PESQ value by 0.25.For other noises, the PESQ value also has a corresponding increase under different signal-to-noise ratios.%为探究不同的噪声对语音增强算法性能的不同影响,提出一种参数自适应维纳滤波语音增强算法,根据不同的噪声类型,设置不同的参数初始值,做不同的噪声功率谱评估.使用深度神经网络对噪声进行分类,得到准确的分类结果;对不同的噪声,得到维纳滤波算法与使用声音活动检测(VAD)进行噪声功率谱评估相结合的语音增强算法的最优系数组合.进行系列实验,客观的评价结果表明,该算法在Babble噪声下,5 db的信噪比时,能够将PESQ值提高0.25,针对其它的噪声与不同信噪比情况,PESQ值也有相应的提高.