结合EEMD与K-SVD字典训练的语音增强算法

Speech enhancement algorithm that combines EEMD and K-SVD dictionary training

该文提出一种总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)方法与K奇异值分解(K-singular value decomposition,K-SVD)字典算法相结合的语音增强算法。将带噪语音通过EEMD分解得到各本征模式分量(intrinsic mode function,IMF),对各IMF分量进行互相关和自相关分析,去除噪声IMF分量,并将过渡IMF分量再次进行EEMD分解,去除其中的噪声IMF分量。将过渡IMF分量和剩余的IMF分量叠加,得到预降噪的带噪语音。利用纯净语音,通过K-SVD字典训练算法得到过完备字典。对预降噪的带噪语音通过过完备字典进行稀疏表示,稀疏系数重构出纯净语音。实验结果表明:在低信噪比和高信噪比情况下,该算法的去噪效果明显优于传统的谱减法、小波阈值去噪法和K-SVD字典训练。

This paper presents a speech enhancement algorithm that combines the ensemble empirical mode decomposition （EEMD） algorithm and the K-singular value decomposition （K-SVD） dictionary training algorithm. The EEMD algorithm is used to obtain the intrinsic mode function （IMF） components from noisy speech. The cross correlations and antocorrelations of each IMF are calculated from the IMF components to filter out the noisy IMF components. The transition IMF components are again decomposed with EEMD to further remove the noisy component. The remained IMFs and transition IMFs are superimposed to generate the de noised speech. An over-complete dictionary is then trained from the clean speech by the K-SVD dictionary training algorithm. The de-noised speech is then sparse decomposed with the over-complete dictionary to obtain the enhanced speech by recovering the speech signal from sparse coefficient vectors. Tests show that the algorithmachieves better de-noising than the traditional spectral subtraction, wavelet threshold de-noising and K-SVD dictionary-training algorithms for both low signal-to-noise ratio （SNR） and high SNR environments.