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Most speech enhancement algorithms heavily depend on the noise power spectral density (PSD). Because this quantity is unknown in practice, estimation from the noisy data is necessary. We present a low complexity method for noise PSD estimation. The algorithm is based on a minimum mean-squared error estimator of the noise magnitude-squared DFT coefficients. Compared to minimum statistics based noise...
In recent years many speech enhancement methods are based on stochastic models. In contrast with most recently models, we do not consider that DFT coefficients may be modeled just by complex Gaussian, Laplacian or Gamma density and stochastic character. We model the DFT coefficients by T location-scale distribution. Then we derive a new MMSE estimator which relies on stochastic-deterministic model...
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