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In [7], Habets et al. presented the joint suppression technique of stationary (e.g., background noise) and non-stationary interference (e.g., echo) using a soft decision approach.
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As opposed to the conventional HMM-based synthesis system, the proposed method uses a soft decision tree structure for the output probability distribution and a hard decision tree for voicing probabilities; therefore, we cannot apply the same tying structure for both voicing and output probabilities in the proposed system.
The pixel structure obtained in this way can be led to a block of both available and estimated pixels using a soft-decision estimation process.
In order to use such a soft decision tree to predict the parameters of the HMM output probability distribution, we derive the smoothest (maximum entropy) distribution which captures all partial first-order moments and a global second-order moment of the training samples.
While previous approaches employ a hard decision between occluded and non-occluded sub-templates, the authors in [16] use a soft decision which exploits special properties of X-ray images.
However, in the soft decision tree construction procedure, the different terminal nodes may cover overlapped regions of the contextual space and splitting a leaf using a soft question affects the parameters of all other leaves.
Do this gently, using a soft cloth.
Wipe dry using a soft cloth.
Dry immediately using a soft cloth.
Clean them using a soft nail brush.
Dab dry using a soft towel.
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