def enhanceAccuracy():
nDescriptors = len(SA.descriptorMapping)
#nDescriptors = 1
iterations = 10
best = 0.0
accuracies = []
for i in range(nDescriptors):
accumulator = 0.0
for itr in range(iterations):
try:
(totalSounds, nIncorrectClassified) = SA.clusterSounds("sounds", nCluster = 10, descInput = [5,12,11,14,9])
accuracy = float(100.0*float(totalSounds-nIncorrectClassified)/totalSounds)
accumulator += accuracy
except:
print("Error at step {}".format(i))
baseline = float(accumulator/10)
accuracies.append(baseline)
if baseline > best:
best = baseline
bestDesc = i
print("Best performance (over {} iterations): {}%, obtained with descriptors (5, 12, 11, 14, 9)".format(iterations, best, bestDesc))
def computeClusteringBaseline():
nDescriptors = len(SA.descriptorMapping)
iterations = 10
accuracies = []
errorsSteps = []
best = 0.0
bestDesc = (-1,-1)
for i in range(nDescriptors):
for j in range(nDescriptors):
if j <= i: # the descriptors are equal or the pair has already been computed
continue
#SA.descriptorPairScatterPlot("sounds", descInput = (i,j), anotOn = 0)
# compute the mean k-means clustering performance over 10 iterations
accumulator = 0.0
for itr in range(iterations):
try:
(totalSounds, nIncorrectClassified) = SA.clusterSounds("sounds", nCluster = 10, descInput = [i,j])
accuracy = float(100.0*float(totalSounds-nIncorrectClassified)/totalSounds)
accumulator += accuracy
except:
print("Error clustering {} and {}".format(SA.descriptorMapping[i], SA.descriptorMapping[j]))
errorsSteps.append((i,j))
pass
baseline = float(accumulator/10)
accuracies.append(baseline)
if baseline > best:
best = baseline
bestDesc = (i,j)
print("Descriptors: {}-{}, {}-{}".format(i, SA.descriptorMapping[i], j, SA.descriptorMapping[j]))
print("Accuracy: {}%".format(round(baseline,2)))
print("Best performance (over {} iterations): {}%, obtained with descriptors {}".format(iterations, best, bestDesc))
return (errorsSteps, accuracies)
import soundAnalysis as SA
SA.clusterSounds('testDownload/', nCluster=3, descInput=[3,11,13])
def cluster(desc):
SA.clusterSounds(Dir, nCluster = 3, descInput = desc)
import soundAnalysis as sa
import matplotlib.pyplot as plt
# 'lowlevel.mfcc.mean.2', 'sfx.logattacktime.mean'
#sa.descriptorPairScatterPlot('joeDown', (3,13))
# 'lowlevel.spectral_contrast.mean.3', 'lowlevel.mfcc.mean.2'
#sa.descriptorPairScatterPlot('joeDown', (13,8))
# 'lowlevel.mfcc.mean.3', 'lowlevel.mfcc.mean.2',
#sa.descriptorPairScatterPlot('joeDown', (13,14))
#[3,8,13,14]
#sa.clusterSounds('joeDown', 3, [3,13])
#sa.clusterSounds('joeDown', 3, [8,13])
sa.clusterSounds('joeDown', 12, [13, 14])
#sa.clusterSounds('joeDown', 3, [11, 11, 14, 1, 3])
#sa.clusterSounds('joeDown', 3, [11, 15, 3])
#sa.clusterSounds('joeDown', 3, [3, 11, 11])
import soundAnalysis as sa
import matplotlib.pyplot as plt
# 'lowlevel.mfcc.mean.2', 'sfx.logattacktime.mean'
#sa.descriptorPairScatterPlot('joeDown', (3,13))
# 'lowlevel.spectral_contrast.mean.3', 'lowlevel.mfcc.mean.2'
#sa.descriptorPairScatterPlot('joeDown', (13,8))
# 'lowlevel.mfcc.mean.3', 'lowlevel.mfcc.mean.2',
#sa.descriptorPairScatterPlot('joeDown', (13,14))
#[3,8,13,14]
#sa.clusterSounds('joeDown', 3, [3,13])
#sa.clusterSounds('joeDown', 3, [8,13])
sa.clusterSounds('joeDown', 12, [13,14])
#sa.clusterSounds('joeDown', 3, [11, 11, 14, 1, 3])
#sa.clusterSounds('joeDown', 3, [11, 15, 3])
#sa.clusterSounds('joeDown', 3, [3, 11, 11])
7: 'lowlevel.spectral_contrast.mean.2',
8: 'lowlevel.spectral_contrast.mean.3',
9: 'lowlevel.spectral_contrast.mean.4',
10: 'lowlevel.spectral_contrast.mean.5',
11: 'lowlevel.mfcc.mean.0',
12: 'lowlevel.mfcc.mean.1',
13: 'lowlevel.mfcc.mean.2',
14: 'lowlevel.mfcc.mean.3',
15: 'lowlevel.mfcc.mean.4',
16: 'lowlevel.mfcc.mean.5'
}
'''
N = 17 # number of descriptors
# Iterate through all the possible pairs for descriptors and see if they cluster the sounds good or they don't
for i in range(N):
for j in range(N):
SA.descriptorPairScatterPlot('downloaded/', descInput=(i, j), anotOn=0)
# Cluster sounds using k-means algorithm
SA.clusterSounds('downloaded/',
nCluster=10,
descInput=[4, 8, 9, 10, 11, 12, 13, 14, 15])
# Final choice of descriptors: 4,8,9,10,11,12,13,14,15
# Mean accuracy of 68.65% using k-means algorithm with 10 clusters
print "-----------"
SD.downloadSoundsFreesound(queryText = "trumpet", API_Key = my_API_Key, outputDir = "./testCluster", topNResults = 2, duration = (0,10), tag = "single-note")
print "-----------"
print "Download of set-4 completed"
print "\n"
#Get sound visual info
SA.showDescriptorMapping()
print "Starting plotting:"
#idx = 1
#while(idx<=16):
# SA.descriptorPairScatterPlot("./testDownload", descInput = (0,idx))
# print "idx = " + str(idx)
# print "\n"
# idx = idx + 1
SA.descriptorPairScatterPlot("./testDownload", descInput = (0,15))
print "Plot completed"
print "\n"
print "Starting clustering:"
SA.clusterSounds("./testDownload", nCluster = 3, descInput = [0,1,14,15])
print "Clustering completed"
print "\n"
print "Starting kNN clustering:"
SA.classifySoundkNN("./testCluster/trumpet/247171/247171_637616-lq.json","./testDownload/", 3, descInput = [0,1,14,15])
print "kNN clustering completed"
print "\n"