Exact(8)
Figure 3 Changes in feature variance with various SNR conditions.
In Figures 3 and 4, we observe some differences in scale between the test feature variance and the averaged variance model.
Therefore, if the feature variance is computed in its acoustic unit level, it should be closer in scale to the average variance model.
After the initial centers are estimated, the feature variance of each region is calculated, and pixels are classified according to their distance from the center of each feature divided by the variance.
It can be seen from Table 3 that an extension of the training data generally improves the performance of DNN-HMM systems for both types of features, presumably since DNNs profit from large feature variance seen during training.
After considering these differences, we observe in Figure 4 that the averaged variance model decreases quite linearly with the SNR conditions of test utterances to compensate for the mismatch between the input feature variance and the variance models.
Similar(52)
The proposed solution was based on adding a controlled amount of noise to "fill in"? the valleys and to reduce the features' variance.
Note that this test is different from the testing hypothesis of μ1 – μ2 = 0 by account of the null (unregulated) log FC distribution, which is supposed to be known and independent from the distribution of regulated features (variance of the null distribution is further defined in the next section, see eq. (12)).
Fold change test was shown [ 7] to be good only if features variances are all fairly similar [ 7].
The independence of fold change test on features variances triggered researchers to look for combined approaches – to require that DE features satisfy both p-value and fold change criteria simultaneously [ 40].
Its performance depends on features variances which can be very different for different pre-processing methods applied to data [ 42], see for example Figure 1 for comparison of MAS5 and RMA pre-processed data.
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