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In conventional speech enhancement algorithms, the most used technique for noise suppression is the attenuating filter, mainly because that the sign or phase of the clean speech and noise coefficients are assumed to be coincident. However, the amplitude of the noisy speech coefficient may not always be bigger than that of the clean speech in fact. Considering the two stats of noise signals in DCT...
The performance of a noisy speech enhancement algorithm depends mainly on the accuracy of the a priori signal-to-noise ratio (SNR) estimate. The decision-directed (DD) algorithm for estimating the a priori SNR has received lots of attention due to its good performance in eliminating the musical noise and the low computational complexity. However, this algorithm has a serious problem in that the estimation...
The Laplacian model factor estimation is a critical link for noisy speech enhancement technique employing Laplacian statistical model priori of clean speech. In this letter, we propose a novel estimation algorithm for this parameter based on soft decision in discrete cosine transform domain. As the speech signal is not always present in the noisy speech signal at all components, we first compute the...
Two novel approaches for Laplacian factor estimation are proposed for noisy speech enhancement employing Laplasian-Gaussian mixture model in discrete cosine transform domain. Based on the property of generalized Gaussian distribution model, the proposed approaches indirectly attain the estimation of Laplacian factor using its relationship with the variance of clean speech components under the Laplacian...
In order to improve the performance of a speech enhancement system, Plapous introduced a novel method called two-step noise reduction (TSNR) technique to refine the a priori SNR estimation of the decision-directed (DD) approach. However, the performance of this method depends on the choice of gain function. In this paper, we propose a modified approach for the a priori SNR estimation in DCT domain...
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