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As suggested earlier, the interferometric imaging condition attenuates both types of noise at once, since it does not explicitly distinguish between the various causes of random fluctuations.
Therefore, it is an important property of impulse noise filters that they are capable of suppressing both types of noise equally well.
Both types of noise degrade the performance of MFCC seriously as the SNR gets worse, while CHN and all of the other HEQ-related algorithms benefit the recognition accuracy significantly.
One of the properties of the examined surveillance dataset is having a decent illumination and nonblurred frames while both types of noise are common to occur in video sequences.
Although both types of noise can be removed in a preprocessing stage by a cascade of low and high pass filters, the filtering itself can introduce artifacts that distort the shape of the pumps.
One of the most effective strategies for reducing both types of noise is data integration.
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Figure 3 illustrates the cross-power spectra for both types of noises.
It is seen that NC-FADF outperformed FADF in both types of noises under almost all SNR conditions.
When the SNR is very low, where the energy of speech mixture is dominated by the noise, the TIR improvement (between target and jammer speech) by NC-FADF contributed less to the overall TINR gains, and here the enhancement processing by TDC-GSub improved TINR greatly in both types of noises.
In Tables 2 and 4, the TIR gains of NC-FADF outperform those of the baseline for both types of noises, at the cost of a slightly decreased SNR, as shown in Tables 3 and 5. Since FADF is a fast and approximate implementation of the baseline ADF, it suffered a slight degradation from the baseline and showed occasional instability in the iterative estimations of separation filters.
Additionally, we would like to find out if both types of noises are present in the intrinsic apoptosis pathway, how the cell-to-cell variability of the delay time of CEA response is influenced.
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