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Noise reduction technologies have been applied to enhance the intelligibility of voice communications. However, existing methods are vulnerable to complex non-stationary noisy conditions, which are commonly encountered in real world hands-free scenarios. Additionally, the existing methods do not fully take the advantage of the deployment of multi-channel microphone arrays on the burgeoning high-end...
A novel noise power spectral density (PSD) estimator for disturbed speech signals which operates in the short-time Fourier domain is presented. A noise PSD estimate is provided by constrained tracing with time of the noisy observation separately for each frequency bin. The constraint is a limitation of the logarithmic magnitude change between successive time frames. Since speech onset is assumed as...
Formant frequency is a one of the most important speech feature, which has widespread applications in speech recognition, synthesis, and compression. In this paper, a new time-frequency domain scheme for the estimation of formant frequencies from noise-corrupted speech signals is presented. In order to overcome the adverse effect of noise, instead of conventional autocorrelation function (ACF), a...
Although noise PSD estimation is a crucial part of noise reduction algorithms, most noise PSD estimators have problems in tracking non-stationary noise sources. Recently, a noise PSD estimator based on DFT-subspace decompositions was proposed, which improves estimation of the PSD of such noise sources. However, as this approach is based on eigenvalue decompositions per DFT bin, it might be too computationally...
In order to eliminate the musical noise remained in the results enhanced by short-time spectral attenuation techniques, this paper proposes a novel a priori SNR estimator. The proposed estimator is built on Burg-based power spectral estimation, which takes into account the properties in estimator of smoothness, accuracy and resolution, with the emphasis on their relationships and influences on de-noising...
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