The s.d. of the single unenhanced LH2 emission level of 15% (see Methods section) was used for the enhancement error bar.
Self-enhancement errors are the proportion of discordances between the medical records and observers where the medical record shows a positive result: either compliance with an indicator such as AMTSL or the non-occurrence of an adverse outcome such as PPH.
For quantities that were calculated from several different raw measurements (e. g., adsorption and fold delivery enhancement), the errors were propagated according to the arithmetic operations performed.
Speech enhancement and error concealment have seen a considerable progress over the past decades.
However, there remains a distinct difference between speech enhancement and error concealment concerning the estimation of the noise power.
So far, we have introduced an application example of recursive MMSE estimation for both speech enhancement and error concealment.
This paper provides new insights into links between speech enhancement and error concealment based on recursive MMSE estimation.
The aim of this paper is to reveal links between the fields of speech enhancement and error concealment focusing on recursive MMSE estimation approaches.
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.
Thus, in both speech enhancement and error concealment, the current observation (Y ℓ (k) or y m (n)) and the channel noise power (density) ((sigma _{D,ell }^{2} k)) or N 0) are needed for likelihood computation (cf. Figures 2 and 3).
One of the reasons that people with LCD screens might feel that they are seeing more defects (hence concluding that LCD is more precise) is that the contrast enhancement can exaggerate errors.
