Titlebook: DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement; Richard C. Hendriks,Timo Gerkmann,Jesper Jensen Book 2013 Sprin

FRET

Applied Multivariate Statistical Analysismated in the past [61, 62]. Krawczyk and Gerkmann [63] have proposed an algorithm to blindly estimate the clean speech phase in voiced speech from a noisy observation. They showed that a blind estimation of the clean speech phase is possible and may push the limits of speech enhancement algorithms f

十字架

Wolfgang Karl Härdle,Léopold Simar . (.) is a function of the clean speech DFT coefficients, such as the magnitude . (.) = A, P (. |.) is the . SPP,E(. (.)|., .) = 0 [85] and E.|Y, .) is realized by the estimators in Sec. 4.4. For a discussion of the special case . = log (A) see also [84]. In this section we aim at deriving estimato

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ETHER

DFT-Based Speech Enhancement Methods-Signal Model and Notation,ch enhancement methods, i.e., we use the DFT as the transform in Figure 2.1. The specific enhancement systems which we consider are rather general, as they impose relatively few assumptions concerning the speech and noise production process; thus, the resulting systems are robust and work well in di

Flatus

Speech DFT Estimators,efficient . at each time-frequency point. Historically, two different estimator classes have been developed, namely, complex-DFT (CDFT) estimators which estimate the complex-valued DFT coefficient . directly, and magnitude-DFT (MDFT) estimators which estimate . = |S|, and append the noisy phase to f

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Speech Presence Probability Estimation,that speech is present in frequency bin . at time segment . while .(.) indicates speech absence. In the sequel we neglect the time and frequency indices for brevity. In Figure 2.1 we see that the speech presence probability (SPP) may be needed for the target estimate, the noise PSD estimate, and the

Endometrium

Noise PSD Estimation,nd . SNR. The quality of the estimated speech signal therefore heavily depends on the accuracy of the noise PSD estimate. The noise PSD can be underestimated or overestimated. An underestimate of . generally leads to an undersuppression of the noisy speech, and an unnecessarily large amount of resid

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Simulation Experiments with Single-Channel Enhancement Systems, an exhaustive comparison of all combinations of sub-algorithms of a speech enhancement system is not possible here. For a comparison of sub-algorithms, such as noise PSD estimators and SPP estimators we refer to the previous sections and the references therein. Instead, in this section we demonstra

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