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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...
One of the most used techniques for estimating the a priori SNR in many speech enhancement systems is the decision-directed (DD) approach, which is famous for the good performance in reducing musical noise with low complexity calculation. However, the conventional DD approach with fixed smoothing factor suffers from the inherent tradeoff between speech distortion elimination and musical noise reduction...
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...
In view of the parameters estimation problem of a priori SNR in speech enhancement, a new method in this paper is proposed by adding momentum term. This algorithm maintains the advantages of the direct decision algorithm in effectiveness and briefness, also improves the tracking speed of the instantaneous SNR, therefore it can further suppress the generation of musical noise. Simulation results under...
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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