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UMVAD improves the classical statistical model-based VAD by utilizing an adaptive threshold to replace the original fixed one and modifies the unbiased MMSE-based noise estimation approach using an adaptive a priori speech presence probability calculated by entropy instead of the original fixed one.
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Furthermore, regulatory bodies typically require that a model is specified in advance when utilizing an adaptive trial design.
Zuo et al. [71] also use an adaptive threshold.
UMVAD adopts an adaptive threshold instead of the fixed threshold of VAD, utilizes the entropy to calculate an adaptive a priori speech presence probability to replace the fixed value of the unbiased MMSE, and adopts different strategies to estimate noise according to whether speech is absent or not.
UMVAD adopts an adaptive threshold instead of the fixed threshold of VAD in [15] and utilizes the entropy to calculate an adaptive a priori SPP P(H 1) to replace the fixed value of the unbiased MMSE in [19].
Moreover, an adaptive threshold is superior to a fixed threshold.
The images are subsequently binarized using an adaptive threshold.
This includes the design of an adaptive threshold.
For volume delineation, we used an adaptive threshold method and three different fixed thresholds for comparison.
Rocks are then segmented from the resulting contrast map by an adaptive threshold.
We combine the color enhancement with an adaptive threshold to extract red regions in the image.
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