Another general approach is cepstral mean normalization (CMN) [6, 7], which has been extensively examined as a simple and effective way of reducing reverberation by normalizing the cepstral features.
The final discriminative feature indicating the likelihood of an image patch having been SDJPEG compressed is derived by normalizing the extracted feature with the reference values s m, n SD and s m, n SS, i.e., s n m, n = s m, n - s m, n SD / s m, n SS - s m, n SD.
In the NN training stage, it is done by normalizing the current reverberant feature vector and the current anechoic feature vector (which is the target of training) to the normalization target.
The segment-based normalization is done by normalizing the current frame of the anechoic and the reverberant feature and also preserving the power envelope of reverberant input segment.
By normalizing the cost structure of certain transactions, UpCounsel believes that it's able to significantly speed up the process.
We normalized the features by subtracting and dividing them by training examples mean and standard deviation, respectively.
This process is done by first applying a scaling to each of the outlooks as to normalize the features to the same range of [0,1].
(3) Normalize the features.
Having been extensively examined, it has been shown to be a simple and effective way of reducing reverberation by normalizing cepstral features.
This means that R is normalized by the features in the steady cusp.
