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The error fraction which can be corrected is at most 50% in traditional codes.
However, due to physical reasons, the error signal must be real-valued and therefore a complex-valued LMS algorithm is run only by a real-valued error fraction.
Here, a new operational algorithm has been developed that employs an inverted gaussian function to estimate error fraction parameters, which are uncorrelated and vary in spatial, spectral and temporal domains.
inclusion fraction —the percentage of correct segments that exceed the threshold (for the speech threshold this refers to speech segments, and for the music threshold this refers to music segments); error fraction —the percentage of incorrect segments that exceed the threshold.
For each of the first four thresholds the following parameters are computed from the training data: (i) inclusion fraction —the percentage of correct segments that exceed the threshold (for the speech threshold this refers to speech segments, and for the music threshold this refers to music segments); (ii) error fraction —the percentage of incorrect segments that exceed the threshold.
We refer to this second statistic as the error fraction (EF).
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Note that by the definition of the extreme thresholds, their error fractions are 0. .
Note that by the definition of the extreme thresholds, their error fractions are 0. With a total of over 20 features computed on the frame level and 4 6 statistical parameters computed per feature on the segment level, the feature space is quite large.
This means that small changes in the data can result in extremely different trees, thus different interpretations, distinct predictions for individual cases and widely varying error fractions.
If the parameters are set suitably, it is possible to achieve an optimal combination of information rate and error correction fraction.
As an approximation of the formal error, the fraction between the mean spectral power (noise power) of the LSP and the peak spectral power (corresponding to the reflector height measurement) of the LSP is taken and multiplied with a factor of 0.2 (see the grey error bars in Figure 5).
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