#!usr/bin/python
from python_speech_features import mfcc
import scipy.io.wavfile as wavv
import numpy as np
def normalizeDataStd(data):
#normalize with mean and std
#norm = (x_i - mean) / std
mean = np.mean(data,axis=0)
std = np.std(data,axis=0)
data = (data - mean) / std
def normalizeDataMM(mean_features):
#normalize with min , max
#norm = (x_i - min ) / (max - min)
dataMin = np.amin(data,axis=0)
dataMax = np.amax(data,axis=0)
base = dataMax - dataMin
data = (data - dataMin) / base
def mfcc_features_extraction(wav):
inputWav,wav = readWavFile(wav)
rate,signal = wavv.read(inputWav)
mfcc_features = mfcc(signal,rate)
#n numpy array with size of the number of frames , each row has one feature vector
return mfcc_features,wav
def mean_features(mfcc_features,wav):
#make a numpy array with length the number of mfcc features
mean_features=np.zeros(len(mfcc_features[0]))
#for one input take the sum of all frames in a specific feature and divide them with the number of frames
for x in range(len(mfcc_features)):
for y in range(len(mfcc_features[x])):
mean_features[y]+=mfcc_features[x][y]
mean_features = (mean_features / len(mfcc_features))
print mean_features
writeFeatures(mean_features,wav)
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 = 'PATH_TO_WAV'+wav
return inputWav,wav
def writeFeatures(mean_features,wav):
#write in a txt file the output vectors of every sample
f = open('mfcc_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,mean_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 main():
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: ')
for x in range(1,int(amount)):
wav = '/'+folder+'/'+str(x)+'.wav'
print wav
mfcc_features,inputWav = mfcc_features_extraction(wav)
mean_features(mfcc_features,inputWav)
main()
