pydrobert.speech.pre
Classes for pre-processing speech signals
- class pydrobert.speech.pre.Dither(coeff=1.0)[source]
Bases:
PreProcessor
Add random noise to a signal tensor
The default axis of apply has been set to None, which will generate random noise for each coefficient. This is likely the desired behaviour. Setting axis to an integer will add random values along 1D slices of that axis.
Intermediate values are calculated as 64-bit floats. The result is cast back to the input data type.
- Parameters:
coeff (
float
) – Standard deviation of dither
- aliases = {'dither', 'dithering'}
- coeff
- class pydrobert.speech.pre.PreProcessor[source]
Bases:
AliasedFactory
A container for pre-processing signals with a transform
- class pydrobert.speech.pre.Preemphasize(coeff=0.97)[source]
Bases:
PreProcessor
Attenuate the low frequencies of a signal by taking sample differences
The following transformation is applied along the target axis
new[i] = old[i] - coeff * old[i-1] for i > 1 new[0] = old[0]
This is essentially a convolution with a Haar wavelet for positive coeff. It emphasizes high frequencies.
Intermediate values are calculated as 64-bit floats. The result is cast back to the input data type.
- Parameters:
coeff (
float
) – Preemphasis coefficient
- aliases = {'preemph', 'preemphasis', 'preemphasize'}
- coeff