-
Notifications
You must be signed in to change notification settings - Fork 1
/
extractFeatures.py
188 lines (148 loc) · 6.7 KB
/
extractFeatures.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
import os
import glob
import sys
import ast
import cPickle as pickle
from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import audioFeatureExtraction as aF
from pyAudioAnalysis import audioTrainTest as aT
import numpy as np
import labelTools
import pdb
import extractFeatures_config
# Feature extraction. Assume for both 159 and daylong
# constant bitrate of files
os.system('mkdir -p ../data/features/')
WAV_PATH = extractFeatures_config.WAV_PATH
DATA_PATH = extractFeatures_config.DATA_PATH
OUTPUT_FEATURES_PATH = extractFeatures_config.OUTPUT_FEATURES_PATH
FOLDS_PATH = extractFeatures_config.FOLDS_PATH
FRAME_WIDTH = extractFeatures_config.FRAME_WIDTH
INV_FRAME_WIDTH = extractFeatures_config.INV_FRAME_WIDTH
MIN_DUR = extractFeatures_config.MIN_DUR
stWin = extractFeatures_config.stWin
stStep = extractFeatures_config.stStep
shortSpeechDict = labelTools.label_map
daylongNearSpeechDict = labelTools.label_map1_near
daylongFarSpeechDict = labelTools.label_map1_far
daylongAllSpeechDict = dict(daylongNearSpeechDict,**daylongFarSpeechDict)
def silMapFn(label,speechDict):
if label in speechDict.keys():
return 1
return 0
label_map = labelTools.label_map
# contains CHI, MOT, FAT, OCH, OAD, OTH
classDict = labelTools.classDict
classDictBinary = labelTools.classDictBinary
classDictTernary = labelTools.classDictTernary
def classMapFn(label,typeSpeechDict):
if label in typeSpeechDict.keys():
label = typeSpeechDict[label]
if label in classDict.keys():
return classDict[label]
return 0
def classMapBinaryFn(label,typeSpeechDict):
if label in typeSpeechDict.keys():
label = typeSpeechDict[label]
if label in classDictBinary.keys():
return classDictBinary[label]
return 0
def classMapTernaryFn(label,typeSpeechDict):
if label in typeSpeechDict.keys():
label = typeSpeechDict[label]
if label in classDictTernary.keys():
return classDictTernary[label]
return 0
def featureListToVectors(featureList):
X = np.array([])
Y = np.array([])
for i, f in enumerate(featureList):
if i == 0:
X = f
Y = i * np.ones((len(f), 1))
else:
X = np.vstack((X, f))
Y = np.append(Y, i * np.ones((len(f), 1)))
return (X, Y)
def basename(filename):
return filename[filename.rfind('/')+1:filename.rfind('.')]
def getFilesFromPortion(portion):
t1 = open(portion,'r').read().split('\n')[:-1]
return [x[:x.find('.')] for x in t1]
foldFileList = []
for portion in glob.glob(FOLDS_PATH):
foldFileList.append([WAV_PATH+x+'.wav' for x in getFilesFromPortion(portion)])
def getTotalAudio(folder_to_wavs):
total = np.asarray([])
print folder_to_wavs
FirstFs = audioBasicIO.readAudioFile(folder_to_wavs[0])[0]
for wav in folder_to_wavs:
[Fs,x] = audioBasicIO.readAudioFile(wav)
if Fs != FirstFs:
print >> sys.stderr, "Inconsistent bitrates in files, found " + str(FirstFs)+" and "+str(Fs)
total = np.concatenate((total,x))
return (Fs,total)
def getTotalEnergyVector(folder_to_wavs): # given a single list of wav paths, return their aggregate 10% vector
[Fs,x] = getTotalAudio(folder_to_wavs)
ShortTermFeatures = aF.stFeatureExtraction(x, Fs, stWin * Fs, stStep * Fs)
EnergySt = ShortTermFeatures[1, :]
E = np.sort(EnergySt)
L1 = int(len(E) / 10)
T1 = np.mean(E[0:L1]) + 0.000000000000001
T2 = np.mean(E[-L1:-1]) + 0.000000000000001 # compute "higher" 10% energy threshold
Class1 = ShortTermFeatures[:, np.where(EnergySt <= T1)[0]] # get all features that correspond to low energy
# Class1 = ShortTermFeatures[1,:][np.where(EnergySt <= T1)[0]] # purely energy
Class2 = ShortTermFeatures[:, np.where(EnergySt >= T2)[0]] # get all features that correspond to high energy
# Class2 = ShortTermFeatures[1,:][np.where(EnergySt >= T2)[0]] # purely energy
featuresSS = [Class1.T, Class2.T] # form the binary classification task
[featuresNormSS, MEANSS, STDSS] = aT.normalizeFeatures(featuresSS) # normalize to 0-mean 1-std
[X,y] = featureListToVectors(featuresNormSS)
return X,y,Fs
def getStVectorPerWav(wavFile,stWin,stStep): # given a wav, get entire sT features
[Fs,x] = getTotalAudio([wavFile])
ShortTermFeatures = aF.stFeatureExtraction(x, Fs, stWin * Fs, stStep * Fs)
[featuresNormSS, MEANSS, STDSS] = aT.normalizeFeatures([ShortTermFeatures]) # normalize to 0-mean 1-std
[X,y] = featureListToVectors([featuresNormSS])
return X,y,Fs
def getRawStVectorPerWav(wavFile,stWin,stStep):
[Fs,x] = audioBasicIO.readAudioFile(wavFile)
return aF.stFeatureExtraction(x, Fs, stWin * Fs, stStep * Fs)
def getLabelPerWav(stmFile,stStep,labelsMapFn,labelsDict):
seg = labelTools.convertToFrames(stmFile)
labels = [labelsMapFn(x[2],labelsDict) for x in seg]
return labels[::int(stStep*INV_FRAME_WIDTH)]
def getRawStDataPerWav(wavFile,stWin,stStep,labelsMapFn):
X = getRawStVectorPerWav(wavFile,stWin,stStep)
y = getLabelPerWav(getStmForWav(wavFile),stStep,labelsMapFn)
X = X[:,:len(y)]
return X,np.asarray(y)
def yieldEnergyFeats(foldFileList):
for i in xrange(len(foldFileList)):
trainlist = []
for j in xrange(len(foldFileList)):
if i!=j:
trainlist.extend(foldFileList[j])
testlist = [WAV_PATH+x+'.wav' for x in foldFileList[i]]
trainlist = [WAV_PATH+x+'.wav' for x in trainlist]
yield getTotalEnergyVector(trainlist),getTotalEnergyVector(testlist)
def getStmForWav(wavFile):
return wavFile[:wavFile.rfind('.')]+'.stm'
def getRootName(wavFile):
wavFile = wavFile[wavFile.rfind('/')+1:]
return wavFile[:wavFile.find('.')]
if __name__ == "__main__":
for i in xrange(len(foldFileList)):
print "Extracting for fold " + str(i)
os.system('mkdir -p '+OUTPUT_FEATURES_PATH+str(i))
for wavFile in foldFileList[i]:
print wavFile
X = getRawStVectorPerWav(wavFile,stWin,stStep)
y_sil = getLabelPerWav(getStmForWav(wavFile),stStep,silMapFn,shortSpeechDict)
y_class = getLabelPerWav(getStmForWav(wavFile),stStep,classMapFn,shortSpeechDict)
y_class2 = getLabelPerWav(getStmForWav(wavFile),stStep,classMapBinaryFn,shortSpeechDict)
y_class3 = getLabelPerWav(getStmForWav(wavFile),stStep,classMapTernaryFn,shortSpeechDict)
pickle.dump(X, open(OUTPUT_FEATURES_PATH+str(i)+'/'+getRootName(wavFile)+'_X','w'))
pickle.dump(y_sil, open(OUTPUT_FEATURES_PATH+str(i)+'/'+getRootName(wavFile)+'_ysil','w'))
pickle.dump(y_class, open(OUTPUT_FEATURES_PATH+str(i)+'/'+getRootName(wavFile)+'_yclass','w'))
pickle.dump(y_class2,open(OUTPUT_FEATURES_PATH+str(i)+'/'+getRootName(wavFile)+'_yclass2','w'))
pickle.dump(y_class3,open(OUTPUT_FEATURES_PATH+str(i)+'/'+getRootName(wavFile)+'_yclass3','w'))