Abstract：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 autocorrelations 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 algorithm achieves 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.
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2017, Vol. 57 
