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For this purpose, a dictionary of clean speech components must be prepared.
This is generally a highly demanding task due to the number of speech components.
The time-frequency characteristics of watermark are modeled by using speech components in the selected region.
The speech components are canceled in the error signals of the adaptive PCA-based blocking.
The cepstrum (16) has already been evaluated for the detection of harmonic speech components.
This method is well known for the separate evaluation of noise and speech components.
From the modified signals, the speech components are distinguished from the nonspeech components by using a simple decision scheme.
Therein, the speech components selection is performed by using the time-frequency support function with a certain energy threshold.
Hence, the weighted output is a combination of the MMSE estimates of the speech components of the two input signals.
The eigenvalues decomposition is applied to the representation obtained by the S-method to separate speech components.
The lower perplexity value for language model indicates a greater ability to predict sequence of speech components.
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