diff --git a/feature_extraction_techniques/mgca.py b/feature_extraction_techniques/mgca.py
new file mode 100644
index 0000000..03fb530
--- /dev/null
+++ b/feature_extraction_techniques/mgca.py
@@ -0,0 +1,95 @@
+#!usr/bin/python
+
+from pysptk.sptk import *
+from scipy.signal import hamming
+import numpy.matlib
+import scipy
+import scipy.io.wavfile as wav
+import numpy as np
+import wave
+from python_speech_features.sigproc import *
+from math import *
+
+def readWavFile(wav):
+	#given a path from the keyboard to read a .wav file
+	#wav = raw_input('Give me the path of the .wav file you want to read: ')
+	inputWav = '/home/gionanide/Theses_2017-2018_2519/MEEI-RainBow'+wav
+	return inputWav
+
+#reading the .wav file (signal file) and extract the information we need 
+def initialize(inputWav):
+	rate , signal  = wav.read(readWavFile(inputWav)) # returns a wave_read object , rate: sampling frequency 
+	sig = wave.open(readWavFile(inputWav))
+	# signal is the numpy 2D array with the date of the .wav file
+	# len(signal) number of samples
+	sampwidth = sig.getsampwidth()
+	print 'The sample rate of the audio is: ',rate
+	print 'Sampwidth: ',sampwidth	
+	return signal ,  rate 
+
+#implementation of the low-pass filter
+def lowPassFilter(signal, coeff=0.97):
+	return np.append(signal[0], signal[1:] - coeff * signal[:-1]) #y[n] = x[n] - a*x[n-1] , a = 0.97 , a>0 for low-pass filters 
+
+
+def preEmphasis(wav):
+	#taking the signal
+	signal , rate = initialize(wav)
+	#Pre-emphasis Stage
+	preEmphasis = 0.97
+	emphasizedSignal = lowPassFilter(signal)
+	Time=np.linspace(0, len(signal)/rate, num=len(signal))
+	EmphasizedTime=np.linspace(0, len(emphasizedSignal)/rate, num=len(emphasizedSignal))
+	return emphasizedSignal, signal , rate
+
+def writeFeatures(mgca_features,wav):
+	#write in a txt file the output vectors of every sample
+	f = open('mel_generalized_features.txt','a')#sample ID
+	#f = open('mfcc_featuresLR.txt','a')#only to initiate the input for the ROC curve
+	wav = makeFormat(wav)
+	np.savetxt(f,mgca_features,newline=",")
+	f.write(wav)
+	f.write('\n')
+	
+
+def makeFormat(wav):
+	#if i want to keep only the gender (male,female)
+	wav = wav.split('/')[1].split('-')[1]
+	#only to make the format for Logistic Regression
+	if (wav=='Female'):
+		wav='1'
+	else:
+		wav='0'
+	return wav
+
+
+def mgca_feature_extraction(wav):
+	#I pre-emphasized the signal with a low pass filter
+	emphasizedSignal,signal,rate = preEmphasis(wav)
+	
+	
+	#and now I have the signal windowed
+	emphasizedSignal*=np.hamming(len(emphasizedSignal))
+	
+	mgca_features = mgcep(emphasizedSignal,order=12)
+
+	writeFeatures(mgca_features,wav)
+		
+
+
+
+def mel_Generalized():
+	folder = raw_input('Give the name of the folder that you want to read data: ')
+	amount = raw_input('Give the number of samples in the specific folder: ')
+	print 'Mel-Generalized Cepstrum analysis github implementation '
+	for x in range(1,int(amount)+1):
+		wav = '/'+folder+'/'+str(x)+'.wav'
+		print wav
+		mgca_feature_extraction(wav)
+		
+		
+
+def main():
+	mel_Generalized()
+
+main()