def __init__(self, svmModelName, svmParameters, verboseMode):
self.labelDict = {'guitar':3, 'cello':2, 'klavier':1}
self.pathDict = {'cello':'../../sandbox/sandbox.samples.audio/cello_mono/', 'guitar':'../../sandbox/sandbox.samples.audio/guitar_new_mono/', 'klavier':'../../sandbox/sandbox.samples.audio/klavier_mono/'}
self.testPathDict = {'cello':'../../sandbox/sandbox.samples.audio/cello_test/', 'guitar':'../../sandbox/sandbox.samples.audio/guitar_test_new/', 'klavier':'../../sandbox/sandbox.samples.audio/klavier_test/'}
self.Tplot = Plot3d()
self.Tplot2 = Plot3d()
self.Tplot3 = Plot3d()
self.verbose = verboseMode
if (self.verbose):
print 'initialized 3D plot'
self.statistic = []
for key in self.labelDict.keys():
tmpList = []
tmpList.append(self.labelDict[key]) #label
tmpList.append(0) # success
tmpList.append(0) # errors
self.statistic.append(tmpList)
#params = '-s 2 -t 2 -h 0 -b 0 -v 3'
self.SVM = AudioSVM(svmModelName, svmParameters, None, verboseMode)
if (self.verbose):
print 'initialized SVM'
class Instrumentrecognition(object):
def __init__(self, svmModelName, svmParameters, verboseMode):
self.labelDict = {'guitar':3, 'cello':2, 'klavier':1}
self.pathDict = {'cello':'../../sandbox/sandbox.samples.audio/cello_mono/', 'guitar':'../../sandbox/sandbox.samples.audio/guitar_new_mono/', 'klavier':'../../sandbox/sandbox.samples.audio/klavier_mono/'}
self.testPathDict = {'cello':'../../sandbox/sandbox.samples.audio/cello_test/', 'guitar':'../../sandbox/sandbox.samples.audio/guitar_test_new/', 'klavier':'../../sandbox/sandbox.samples.audio/klavier_test/'}
self.Tplot = Plot3d()
self.Tplot2 = Plot3d()
self.Tplot3 = Plot3d()
self.verbose = verboseMode
if (self.verbose):
print 'initialized 3D plot'
self.statistic = []
for key in self.labelDict.keys():
tmpList = []
tmpList.append(self.labelDict[key]) #label
tmpList.append(0) # success
tmpList.append(0) # errors
self.statistic.append(tmpList)
#params = '-s 2 -t 2 -h 0 -b 0 -v 3'
self.SVM = AudioSVM(svmModelName, svmParameters, None, verboseMode)
if (self.verbose):
print 'initialized SVM'
def loadAllFiles(self, fileName=None):
fileReadable = False
if fileName != None:
if pathHelper.isfile(fileName):
try:
self.loadFeaturesFromFile(fileName)
fileReadable = True
except IOError as e:
print 'file not readable, continue with reading the model new from samples'
if fileReadable == False:
self.loadCelloFiles()
self.loadGuitarFiles()
self.loadPianoFiles()
self.SVM.normalizeFeatureMatrix()
def loadFeaturesFromFile(self, fileName):
file = open(fileName, 'r')
for line in file:
tmpString = line.split(' ') #+1 1:0.3003 2:0.39393
label = float(tmpString[0])
feature = []
print type(tmpString)
for i in xrange(1, len(tmpString) - 1):
featureSplit = tmpString[i].split(':')
print featureSplit
feature.append(float(featureSplit[1]))
self.SVM.addFeature(label, feature)
if label==self.labelDict['guitar']:
self.Tplot.addFeature(feature)
if label==self.labelDict['cello']:
self.Tplot2.addFeature(feature)
if label==self.labelDict['klavier']:
self.Tplot3.addFeature(feature)
self.PCAMatrix=PCA(n_components=2).fit(self.Tplot.allFeatures)
self.Tplot.allFeatures=self.PCAMatrix.transform(self.Tplot.allFeatures)
self.PCAMatrix2=PCA(n_components=2).fit(self.Tplot2.allFeatures)
self.Tplot2.allFeatures=self.PCAMatrix2.transform(self.Tplot2.allFeatures)
self.PCAMatrix3=PCA(n_components=2).fit(self.Tplot3.allFeatures)
self.Tplot3.allFeatures=self.PCAMatrix3.transform(self.Tplot3.allFeatures)
file.close()
def loadCelloFiles(self):
print 'start processing cello files'
count = 0
for fileName in os.listdir(self.pathDict['cello']):
fileName = str(self.pathDict['cello']) + str(fileName)
if fileName.endswith('.wav'):
if (self.verbose):
print 'processing ' + str(fileName)
sPlot = SoundPlot(fileName)
self.Tplot.addFeature(sPlot.features)
self.SVM.addFeature(self.labelDict['cello'], sPlot.features)
if (self.verbose):
print 'extracted unnormalized features: ' + str(sPlot.features)
count += 1
print 'finished processing guitar files - ' +str(count)+' in total'
def loadGuitarFiles(self):
print 'start processing guitar files'
count = 0
for fileName in os.listdir(self.pathDict['guitar']):
fileName = self.pathDict['guitar'] + str(fileName)
if fileName.endswith('.wav'):
if (self.verbose):
print 'processing ' + str(fileName)
sPlot = SoundPlot(fileName)
self.Tplot2.addFeature(sPlot.features)
self.SVM.addFeature(self.labelDict['guitar'], sPlot.features)
if (self.verbose):
print 'extracted unnormalized features: ' + str(sPlot.features)
count += 1
print 'finished processing guitar files - ' +str(count)+' in total'
def loadPianoFiles(self):
print 'start processing piano files'
count = 0
for fileName in os.listdir(self.pathDict['klavier']):
fileName = str(self.pathDict['klavier']) + str(fileName)
if fileName.endswith('.wav'):
if (self.verbose):
print 'processing ' + str(fileName)
sPlot = SoundPlot(fileName)
self.Tplot3.addFeature(sPlot.features)
self.SVM.addFeature(self.labelDict['klavier'], sPlot.features)
if (self.verbose):
print 'extracted unnormalized features: ' + str(sPlot.features)
count += 1
print 'finished processing guitar files - ' +str(count)+' in total'
def printSVMFeatures(self):
print 'printing SVM features'
self.SVM.printFeatures()
def trainSVM(self):
print 'starting SVM training'
self.svmModelName = self.SVM.trainModell()
print 'model saved unter following name: ' + str(self.svmModelName)
def predictAll(self, dumpToFile=False, filePath=None):
self.predictCello(dumpToFile, filePath)
self.predictGuitar(dumpToFile, filePath)
self.predictPiano(dumpToFile, filePath)
def predictCello(self, dumpToFile=None, filePath=None):
print 'try to predict a cello'
for fileName in os.listdir(self.testPathDict['cello']):
fileName = str(self.testPathDict['cello']) + str(fileName)
if fileName.endswith('.wav'):
if (self.verbose):
print 'processing ' + str(fileName)
sPlot = SoundPlot(fileName)
if dumpToFile:
self.SVM.dumpTestDataToFile(filePath, self.labelDict['cello'], sPlot.features)
p_label, p_acc, p_val = self.SVM.predict(self.labelDict['cello'], sPlot.features)
print 'expected\t' + str(self.labelDict['cello'])
print 'actual\t' + str(p_label)
if self.labelDict['cello'] == p_label[0]:
self.statistic[self.labelDict['cello'] - 1][1] = self.statistic[self.labelDict['cello'] - 1][1] + 1
else:
self.statistic[self.labelDict['cello'] - 1][2] = self.statistic[self.labelDict['cello'] - 1][2] + 1
print 'accuracy\t' + str(p_acc)
def predictGuitar(self, dumpToFile=None, filePath=None):
print 'try to predict a guitar'
for fileName in os.listdir(self.testPathDict['guitar']):
fileName = str(self.testPathDict['guitar']) + str(fileName)
if fileName.endswith('.wav'):
if (self.verbose):
print 'processing ' + str(fileName)
sPlot = SoundPlot(fileName)
if dumpToFile:
self.SVM.dumpTestDataToFile(filePath, self.labelDict['guitar'], sPlot.features)
p_label, p_acc, p_val = self.SVM.predict(self.labelDict['guitar'], sPlot.features)
print 'expected\t' + str(self.labelDict['guitar'])
print 'actual\t' + str(p_label)
print 'accuracy\t' + str(p_acc)
if self.labelDict['guitar'] == p_label[0]:
self.statistic[self.labelDict['guitar'] - 1][1] = self.statistic[self.labelDict['guitar'] - 1][1] + 1
else:
self.statistic[self.labelDict['guitar'] - 1][2] = self.statistic[self.labelDict['guitar'] - 1][2] + 1
def predictPiano(self, dumpToFile=None, filePath=None):
print 'try to predict a piano'
for fileName in os.listdir(self.testPathDict['klavier']):
fileName = str(self.testPathDict['klavier']) + str(fileName)
if fileName.endswith('.wav'):
if (self.verbose):
print 'processing ' + str(fileName)
sPlot = SoundPlot(fileName)
if dumpToFile:
self.SVM.dumpTestDataToFile(filePath, self.labelDict['klavier'], sPlot.features)
p_label, p_acc, p_val = self.SVM.predict(self.labelDict['klavier'], sPlot.features)
print 'expected\t' + str(self.labelDict['klavier'])
print 'actual\t' + str(p_label)
print 'accuracy\t' + str(p_acc)
if self.labelDict['klavier'] == p_label[0]:
self.statistic[self.labelDict['klavier'] - 1][1] = self.statistic[self.labelDict['klavier'] - 1][1] + 1
else:
self.statistic[self.labelDict['klavier'] - 1][2] = self.statistic[self.labelDict['klavier'] - 1][2] + 1
def print3DPlot(self):
print 'starting 3D plot'
Tplots = [self.Tplot, self.Tplot2, self.Tplot3]
self.Tplot.multiplePlot(Tplots)
def printStatistics(self):
print self.labelDict
for cl in self.statistic:
print 'values for class:\t' + str(cl[0])
print 'success:\t\t' + str(float(cl[1])) + '\t' + str(float((cl[1] * 100)) / (cl[1] + cl[2])) + '%'
print 'error:\t\t' + str(float(cl[2])) + '\t' + str(float((cl[2] * 100)) / (cl[1] + cl[2])) + '%'
totalError = 0.0
totalSuccess = 0.0
for label in self.statistic:
totalError = totalError + label[2]
totalSuccess = totalSuccess + label[1]
print 'totalError:\t\t' + str(totalError) + '\t' + str((totalError * 100 / (totalError + totalSuccess))) + '%'
print 'totalSuccess:\t' + str(totalSuccess) + '\t' + str((totalSuccess * 100 / (totalError + totalSuccess))) + '%'
print 'crossvalidation accurancy:\t' + str(self.SVM.crossValid)
