# postprocessing¶

Processing module for signal processing operations.

This module demonstrates documentation for the signal processing function which are required as internal computations in the package.

ivar preemphasis:
Preemphasising on the signal. This is a preprocessing step.
ivar stack_frames:
Create stacking frames from the raw signal.
ivar fft_spectrum:
Calculation of the Fast Fourier Transform.
ivar power_spectrum:
Power Spectrum calculation.
ivar log_power_spectrum:
Log Power Spectrum calculation.
ivar derivative_extraction:
Calculation of the derivative of the extracted featurs.
ivar cmvn:Cepstral mean variance normalization. This is a post processing operation.
ivar cmvnw:Cepstral mean variance normalization over the sliding window. This is a post processing operation.

## Global Cepstral Mean and Variance Normalization¶

speechpy.processing.cmvn(vec, variance_normalization=False)[source]
This function is aimed to perform global cepstral mean and
variance normalization (CMVN) on input feature vector “vec”. The code assumes that there is one observation per row.
Parameters: vec (array) – input feature matrix (size:(num_observation,num_features)) variance_normalization (bool) – If the variance normilization should be performed or not. The mean(or mean+variance) normalized feature vector. array

## Local Cepstral Mean and Variance Normalization over Sliding Window¶

speechpy.processing.cmvnw(vec, win_size=301, variance_normalization=False)[source]

This function is aimed to perform local cepstral mean and variance normalization on a sliding window. The code assumes that there is one observation per row.

Parameters: vec (array) – input feature matrix (size:(num_observation,num_features)) win_size (int) – The size of sliding window for local normalization. Default=301 which is around 3s if 100 Hz rate is considered(== 10ms frame stide) variance_normalization (bool) – If the variance normilization should be performed or not. The mean(or mean+variance) normalized feature vector. array