Sentence examples for concealment based on from inspiring English sources

This paper provides new insights into links between speech enhancement and error concealment based on recursive MMSE estimation.

It turns out that recent approaches to bit error concealment based on a predictor are well comparable to iterative approaches in speech enhancement, such as the Kalman filter approach.

Section 1 gives an example for employing recursive MMSE estimation in error concealment in the time domain, presented in strong analogy to Section 1. Section 1 discusses links between speech enhancement and error concealment based on the recursive MMSE approaches from the previous Sections 1 and 1.

The results of the proposed concealment method are compared to those of spatial concealment (SC, based on edge directed filter [7]), and temporal concealment (TC, based on MCTI [11]).

Therefore, we will first observe the relationship between views and propose our error concealment algorithm based on the observation.

The grading of allocation concealment was based on the Cochrane approach (i.e. adequate or uncertain or clearly inadequate).

Studies will be deemed to be at the highest risk of bias if they are scored as at high or unclear risk of bias for either the sequence generation or allocation concealment domains, based on growing empirical evidence that these factors are particularly important potential sources of bias.

The proposed error concealment algorithm is based on this observation.

Our solution provides a fine-grained masking and concealment, which is based on access level privileges of the user.

Most error concealment techniques are based on block matching algorithms [37] or adaptive techniques in unit of block such as [38].

A very common concealment strategy is based on the fact that when the packet loss affects an intra-frame encoded image, and a portion of the frame is properly decoded, the remaining part is replaced with the last row correctly decoded.