Sentence examples for weighted prediction from inspiring English sources

In [38, 39], weighted prediction error (WPE) plus beamforming yielded good performances for REVERB challenge and CHiME-3 challenge [40], respectively.

The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions.

This means that the precision weighted prediction errors do not elicit any action or birdsong, enabling the bird to listen to its companion.

In neural network terms, Eq. (A.3) says that error-units compute the difference between expectations at one level and predictions from the level above (where ξ (i ) are precision weighted prediction errors at the ith level of the hierarchy).

We conducted two analyses: an unweighted analysis where phthalate distributions were estimated with changes in the means of these distributions as a function of demographic variables, and a weighted prediction for the general population in which weights were assigned for a subset of the population depending on the frequency of their demographic variables in the general U.S. population.

Robustified RLS algorithm, based on M robust principle, the so-called robustified recursive least square method (RRLS), uses the sum of weighted prediction errors as the performance index, where the weights are functions of prediction residuals [24 26].

In doing so, it should be noted that the unweighted and weighted predictions are quite similar and appear to fit the data in an unbiased fashion through approximately 75 weeks.

Weighted predictions of annual average PM2.5 concentrations and corresponding prediction variances were generated for each respondent using the fitted semivariogram models corresponding to the NHIS interview year.

The autoregressive parameter is (phi, ) while the signal-noise ratio, (q=sigma _{eta }^{2}/sigma _{varepsilon }^{2},) plays the key role in determining how observations should be weighted for prediction and signal extraction.

The prediction matrices are optimized for each utterance by minimizing the power of an iteratively re-weighted prediction error.

The prediction matrices, (boldsymbol {G}_{T_{bot }}, cdots, boldsymbol {G}_{T_{top }}), are optimized for each utterance by minimizing an iteratively re-weighted prediction error criterion [19].