Sentence examples for ar spectra from inspiring English sources

Typically AR spectra are not sensitive to the model order when the order is above a certain value.

These methods in general parameterize an AR spectra using a least square errors method.

Figures 2 and 3 show the average AR spectra and spectral differences, using five utterances from each group.

In the most cases WVD spectra was useful for searching the optimal model order of AR spectra but sometimes we needed also FFT method to recognize the main features of spectra.

The average AR spectra obtained from /aa/ utterances for each subject were first normalized to the strongest peak between 500 and 1100 Hz to ensure that data across subjects were comparable to each other regardless of their absolute power.

FFT, WVD, and AR spectra were computed for each subject, and several cross-sections of time frequency plots were obtained in order to determine the minimum AR model order required for the AR spectrum to show the same main features evident in FFT and WVD spectra.

To represent vocal tract-related spectral shape, an average autoregressive (AR) spectrum and the spectral difference between normal and pathological subjects are plotted.

Another observation from the average AR spectrum is that the positions of peaks in the middle frequency band are not similar to one another.

The processing of the experimental results enabled (i) various AR spectrum analysis methods and especially Burg's algorithm to be tested, and (ii) conventional spectrum analysis techniques such as the correlogram to be compared with parametric methods at a detection level as well as for mechanical component fault monitoring, especially ball bearing defects.

The equivalence of linear prediction and AR spectrum estimation was then exploited to show that AR spectrum from subbands offers a gain over fullband AR spectrum estimation.

However, it is considered straightforward to apply the method to any number of subbands, and to AR spectrum estimation in subbands.