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Speech signal is corrupted unavoidably by noisy environment in subway, factory, and restaurant or speech from other speakers in speech communication. Speech enhancement methods have been widely studied to minimize noise influence in different linear transform domain, such as discrete Fourier transform domain, Karhunen-Loeve transform domain or discrete cosine transform domain. Kernel method as a nonlinear...
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...
This paper investigates the correlation between successive speech components across time as well as frequency in DCT domain, and proposes a novel speech model for enhancing noisy speech, which assumes the sequence of speech components among successive frames to be a highly correlated and non- stationary process. Based on this model, a linear estimator of clean speech components is obtained from the...
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...
This paper investigates the time correlation between successive speech components and presents a novel speech enhancement algorithm taking into account the inter-frame dependence information in the discrete cosine transform (DCT) domain. The algorithm does not rely on any statistic model and can efficiently attain the optimal estimation of clean speech components from successive noisy speech components...
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