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Estimating the amplitude spectral of noise signal is a very important part in many noise reduction systems. The conventional voice activity detection (VAD)-based method updates the amplitude spectral estimate only in speech absence areas and fails to deal with non-stationary noise. To overcome this problem, this paper proposes two methods to estimate the noise amplitude spectral for non-stationary...
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 a priori signal-noise-ratio (SNR) estimator plays a very crucial role in the performance of a noise reduction system. The decision-directed (DD) approach, which is the most widely used technique for estimating the a priori SNR, suffers from one-frame delay bias when following the a posteriori SNR. Many modifications of the DD approach in the literature focus on accelerating the tracking speed...
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 estimation of the a priori signal-to-noise ratio (SNR) is a very significant issue for many speech enhancement algorithms. The widely-used decision-directed (DD) algorithm largely depresses the musical noise, but the estimated a priori SNR suffer from one frame delay which results in the degradation of speech quality. In this paper, we propose a novel algorithm to a priori SNR estimation which...
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...
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