correction for cmvnw function

docs/source/content/features.rst

@@ -4,29 +4,29 @@
 Features
 =========
 
-.. automodule:: speechpy.main
-.. currentmodule:: speechpy.main
+.. automodule:: speechpy.feature
+.. currentmodule:: speechpy.feature
 
 
 :hidden:`MFCC`
 ~~~~~~~~~~~~~~
 
-.. autofunction:: speechpy.main.mfcc
+.. autofunction:: speechpy.feature.mfcc
 
 
 :hidden:`Mel Frequency Energy`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: speechpy.main.mfe
+.. autofunction:: speechpy.feature.mfe
 
 
 :hidden:`Log Mel Frequency Energy`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: speechpy.main.lmfe
+.. autofunction:: speechpy.feature.lmfe
 
 
 :hidden:`Extract Derivative Features`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: speechpy.main.extract_derivative_feature
+.. autofunction:: speechpy.feature.extract_derivative_feature

docs/source/content/features.rst~

@@ -0,0 +1,32 @@
+.. role:: hidden
+    :class: hidden-section
+
+Features
+=========
+
+.. automodule:: speechpy.main
+.. currentmodule:: speechpy.main
+
+
+:hidden:`MFCC`
+~~~~~~~~~~~~~~
+
+.. autofunction:: speechpy.main.mfcc
+
+
+:hidden:`Mel Frequency Energy`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: speechpy.main.mfe
+
+
+:hidden:`Log Mel Frequency Energy`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: speechpy.main.lmfe
+
+
+:hidden:`Extract Derivative Features`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: speechpy.main.extract_derivative_feature

speechpy/__init__.py

@@ -1,3 +1,2 @@
-from .main import *
-from .processing import *
-
+import feature
+import processing

speechpy/functions.py~

@@ -0,0 +1,43 @@
+from __future__ import division
+import numpy as np
+from . import feature
+from scipy.fftpack import dct
+import math
+
+
+def frequency_to_mel(f):
+    """converting from frequency to Mel scale.
+
+    :param f: The frequency values(or a single frequency) in Hz.
+    :returns: The mel scale values(or a single mel).
+    """
+    return 1127 * np.log(1 + f / 700.)
+
+
+def mel_to_frequency(mel):
+    """converting from Mel scale to frequency.
+
+    :param mel: The mel scale values(or a single mel).
+    :returns: The frequency values(or a single frequency) in Hz.
+    """
+    return 700 * (np.exp(mel / 1127.0) - 1)
+
+
+def triangle(x, left, middle, right):
+    out = np.zeros(x.shape)
+    out[x <= left] = 0
+    out[x >= right] = 0
+    first_half = np.logical_and(left < x, x <= middle)
+    out[first_half] = (x[first_half] - left) / (middle - left)
+    second_half = np.logical_and(middle <= x, x < right)
+    out[second_half] = (right - x[second_half]) / (right - middle)
+    return out
+
+
+def zero_handling(x):
+    """
+    This function handle the issue with zero values if the are exposed to become an argument for any lof function.
+    :param x: The vector.
+    :return: The vector with zeros substituted with epsilon values.
+    """
+    return np.where(x == 0, np.finfo(float).eps, x)

tests/test_local.py

@@ -12,24 +12,29 @@
 fs, signal = wav.read(file_name)
 signal = signal[:,0]
 
+# Example of staching frames
+frames = speechpy.processing.stack_frames(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01, Filter=lambda x: np.ones((x,)),
+         zero_padding=True)
+
+# Example of extracting power spectrum
+frames = speechpy.processing.power_spectrum(frames, fft_length=512)
+
 ############# Extract MFCC features #############
-mfcc = speechpy.mfcc(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01,
+mfcc = speechpy.feature.mfcc(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01,
              num_filters=40, fft_length=512, low_frequency=0, high_frequency=None)
-mfcc_cmvn = speechpy.cmvnw(mfcc,win_size=301,variance_normalization=True)
+mfcc_cmvn = speechpy.processing.cmvnw(mfcc,win_size=301,variance_normalization=True)
 print('mfcc(mean + variance normalized) feature shape=', mfcc_cmvn.shape)
 
-mfcc_feature_cube = speechpy.extract_derivative_feature(mfcc)
+mfcc_feature_cube = speechpy.feature.extract_derivative_feature(mfcc)
 print('mfcc feature cube shape=', mfcc_feature_cube.shape)
 
 ############# Extract logenergy features #############
-logenergy = speechpy.lmfe(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01,
+logenergy = speechpy.feature.lmfe(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01,
              num_filters=40, fft_length=512, low_frequency=0, high_frequency=None)
-logenergy_feature_cube = speechpy.extract_derivative_feature(logenergy)
+logenergy_feature_cube = speechpy.feature.extract_derivative_feature(logenergy)
 print('logenergy features=', logenergy.shape)
 
-# Example of staching frames
-signal = speechpy.stack_frames(signal, sampling_frequency=fs, frame_length=0.020, frame_stride=0.01, Filter=lambda x: np.ones((x,)),
-         zero_padding=True)
+