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Partition the audio signal into frames of size 4,096 samples.
Two hundred fifty highly textured colored frames of size 576 × 720 pels are corrupted by artificial opacities.
We perform the test on the whole sequence as described in Section 3, using a sliding window on frames of size 20, to compute the 3D-FE features.
In the experiments, we use the grayscale version of the cropped frames of size 150×120 by considering the center coordinate of the aligned frames as the face center.
After taking two-level DWT of the host signal, the coefficients in the second-level approximation and detail subbands are both partitioned into non-overlapping frames of size L f.
First, the clean speech training utterances, resampled at 8 kHz, were segmented into 50% overlapped Hann windowed frames of size 256 samples each, corresponding to a duration of 32 ms wherein the speech signal can be assumed stationary.
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A Hamming windowed frame of size 1024 samples is utilized (23.2 milliseconds) with no overlapping between sequential frames.
PE R th = ∑ C j = 1 M Prob { packet in a frame of size Ĉ j } × FER ( Ĉ j ) = FE R th.
In total, there are 58 standard deviations for 58 vectors of size 1584 that need to be transmitted as metadata for each frame of size 352×288.
Thus, the quantity E(k) in (27) represents a measure of the local variance of prediction residuals at the given sliding data frame of size L, and it contains the information about the degree of data non-stationarity.
This means that no more than one outlier, in average, is in the sliding data frame of size L = 5 during the robustified EPE calculation in (31) and MAD calculation in (9), respectively, when the fraction of outliers is 1%%.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com