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Speech enhancement of non-stationary noise based on controlled forward moving average

A pre and post processing technique is proposed to enhance the speech signal of highly non-stationary noisy speech. The purpose of this research has been to build on current speech enhancement algorithms to produce an improved algorithm for enhancement of speech contaminated with non-stationary babble type noise. The pre processing involves two stages. In stage one, the variance of the noisy speech spectrum is reduced by utilizing the Discrete or Prolate Spheroidal Sequence (DPSS) multi-taper algorithm plus a Controlled Forward Moving Average (CFMA) technique. We introduced the CFMA algorithm to smooth and reduce variance of the estimated non-stationary noise spectrum. In the second stage the noisy speech power spectrum is de-noised by applying Stein's Unbiased Risk Estimator (SURE) wavelet thresholding technique. In the third layer, use is made of a noise estimation algorithm with rapid adaptation for a highly non-stationary noise environment. The noise estimate is updated in three frequency sub-bands, by averaging the noisy speech power spectrum using a frequency dependent smoothing factor, which is adjusted, based on a signal presence probability factor. In the fourth layer a spectral subtraction algorithm is used to enhance the speech signal, by subtracting each estimated noise from the original noisy speech. The new proposed post processing is then applied to the complete signal when the speech enhancement is processed using segmental speech enhancement. The enhanced signal is further improved by applying a soft wavelet thresholding technique to the un-segmented enhanced speech at the final processing stage. The results show improvements both quantitatively and qualitatively compared to the speech enhancement that does not apply the CFMA algorithm.