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audioVisualization.py
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audioVisualization.py
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from __future__ import print_function
import shutil, struct, simplejson
from scipy.spatial import distance
from pylab import *
import ntpath
from pyAudioAnalysis import audioFeatureExtraction as aF
from pyAudioAnalysis import audioTrainTest as aT
import sklearn
import sklearn.discriminant_analysis
import os
import sys
def generateColorMap():
'''
This function generates a 256 jet colormap of HTML-like
hex string colors (e.g. FF88AA)
'''
Map = cm.jet(np.arange(256))
stringColors = []
for i in range(Map.shape[0]):
rgb = (int(255*Map[i][0]), int(255*Map[i][1]), int(255*Map[i][2]))
if (sys.version_info > (3, 0)):
stringColors.append((struct.pack('BBB', *rgb).hex())) # python 3
else:
stringColors.append(
struct.pack('BBB', *rgb).encode('hex')) # python2
return stringColors
def levenshtein(str1, s2):
'''
Distance between two strings
'''
N1 = len(str1)
N2 = len(s2)
stringRange = [range(N1 + 1)] * (N2 + 1)
for i in range(N2 + 1):
stringRange[i] = range(i,i + N1 + 1)
for i in range(0,N2):
for j in range(0,N1):
if str1[j] == s2[i]:
stringRange[i+1][j+1] = min(stringRange[i+1][j] + 1,
stringRange[i][j+1] + 1,
stringRange[i][j])
else:
stringRange[i+1][j+1] = min(stringRange[i+1][j] + 1,
stringRange[i][j+1] + 1,
stringRange[i][j] + 1)
return stringRange[N2][N1]
def text_list_to_colors(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
# STEP A: compute strings distance between all combnations of strings
Dnames = np.zeros( (len(names), len(names)) )
for i in range(len(names)):
for j in range(len(names)):
Dnames[i,j] = 1 - 2.0 * levenshtein(names[i], names[j]) / float(len(names[i]+names[j]))
# STEP B: pca dimanesionality reduction to a single-dimension (from the distance space)
pca = sklearn.decomposition.PCA(n_components = 1)
pca.fit(Dnames)
# STEP C: mapping of 1-dimensional values to colors in a jet-colormap
textToColor = pca.transform(Dnames)
textToColor = 255 * (textToColor - textToColor.min()) / (textToColor.max() - textToColor.min())
textmaps = generateColorMap();
colors = [textmaps[int(c)] for c in textToColor]
return colors
def text_list_to_colors_simple(names):
'''
Generates a list of colors based on a list of names (strings). Similar strings correspond to similar colors.
'''
uNames = list(set(names))
uNames.sort()
textToColor = [ uNames.index(n) for n in names ]
textToColor = np.array(textToColor)
textToColor = 255 * (textToColor - textToColor.min()) / \
(textToColor.max() - textToColor.min())
textmaps = generateColorMap();
colors = [textmaps[int(c)] for c in textToColor]
return colors
def chordialDiagram(fileStr, SM, Threshold, names, namesCategories):
'''
Generates a d3js chordial diagram that illustrates similarites
'''
colors = text_list_to_colors_simple(namesCategories)
SM2 = SM.copy()
SM2 = (SM2 + SM2.T) / 2.0
for i in range(SM2.shape[0]):
M = Threshold
# a = np.sort(SM2[i,:])[::-1]
# M = np.mean(a[0:int(SM2.shape[1]/3+1)])
SM2[i, SM2[i, :] < M] = 0;
dirChordial = fileStr + "_Chordial"
if not os.path.isdir(dirChordial):
os.mkdir(dirChordial)
jsonPath = dirChordial + os.sep + "matrix.json"
namesPath = dirChordial + os.sep + "Names.csv"
jsonSMMatrix = simplejson.dumps(SM2.tolist())
f = open(jsonPath,'w'); f.write(jsonSMMatrix); f.close()
f = open(namesPath,'w'); f.write("name,color\n");
for i, n in enumerate(names):
f.write("{0:s},{1:s}\n".format(n,"#"+str(colors[i])))
f.close()
shutil.copyfile(os.path.join(os.path.dirname(os.path.realpath(__file__)),
"data", "similarities.html"),
dirChordial+os.sep+"similarities.html")
shutil.copyfile(os.path.join(os.path.dirname(os.path.realpath(__file__)),
"data",
"style.css"),
dirChordial+os.sep+"style.css")
def visualizeFeaturesFolder(folder, dimReductionMethod, priorKnowledge = "none"):
'''
This function generates a chordial visualization for the recordings of the provided path.
ARGUMENTS:
- folder: path of the folder that contains the WAV files to be processed
- dimReductionMethod: method used to reduce the dimension of the initial feature space before computing the similarity.
- priorKnowledge: if this is set equal to "artist"
'''
if dimReductionMethod=="pca":
allMtFeatures, wavFilesList, _ = aF.dirWavFeatureExtraction(folder, 30.0, 30.0, 0.050, 0.050, compute_beat = True)
if allMtFeatures.shape[0]==0:
print("Error: No data found! Check input folder")
return
namesCategoryToVisualize = [ntpath.basename(w).replace('.wav','').split(" --- ")[0] for w in wavFilesList];
namesToVisualize = [ntpath.basename(w).replace('.wav','') for w in wavFilesList];
(F, MEAN, STD) = aT.normalizeFeatures([allMtFeatures])
F = np.concatenate(F)
# check that the new PCA dimension is at most equal to the number of samples
K1 = 2
K2 = 10
if K1 > F.shape[0]:
K1 = F.shape[0]
if K2 > F.shape[0]:
K2 = F.shape[0]
pca1 = sklearn.decomposition.PCA(n_components = K1)
pca1.fit(F)
pca2 = sklearn.decomposition.PCA(n_components = K2)
pca2.fit(F)
finalDims = pca1.transform(F)
finalDims2 = pca2.transform(F)
else:
allMtFeatures, Ys, wavFilesList = aF.dirWavFeatureExtractionNoAveraging(folder, 20.0, 5.0, 0.040, 0.040) # long-term statistics cannot be applied in this context (LDA needs mid-term features)
if allMtFeatures.shape[0]==0:
print("Error: No data found! Check input folder")
return
namesCategoryToVisualize = [ntpath.basename(w).replace('.wav','').split(" --- ")[0] for w in wavFilesList];
namesToVisualize = [ntpath.basename(w).replace('.wav','') for w in wavFilesList];
ldaLabels = Ys
if priorKnowledge=="artist":
uNamesCategoryToVisualize = list(set(namesCategoryToVisualize))
YsNew = np.zeros( Ys.shape )
for i, uname in enumerate(uNamesCategoryToVisualize): # for each unique artist name:
indicesUCategories = [j for j, x in enumerate(namesCategoryToVisualize) if x == uname]
for j in indicesUCategories:
indices = np.nonzero(Ys==j)
YsNew[indices] = i
ldaLabels = YsNew
(F, MEAN, STD) = aT.normalizeFeatures([allMtFeatures])
F = np.array(F[0])
clf = sklearn.discriminant_analysis.LinearDiscriminantAnalysis(n_components=10)
clf.fit(F, ldaLabels)
reducedDims = clf.transform(F)
pca = sklearn.decomposition.PCA(n_components = 2)
pca.fit(reducedDims)
reducedDims = pca.transform(reducedDims)
# TODO: CHECK THIS ... SHOULD LDA USED IN SEMI-SUPERVISED ONLY????
uLabels = np.sort(np.unique((Ys))) # uLabels must have as many labels as the number of wavFilesList elements
reducedDimsAvg = np.zeros( (uLabels.shape[0], reducedDims.shape[1] ) )
finalDims = np.zeros( (uLabels.shape[0], 2) )
for i, u in enumerate(uLabels):
indices = [j for j, x in enumerate(Ys) if x == u]
f = reducedDims[indices, :]
finalDims[i, :] = f.mean(axis=0)
finalDims2 = reducedDims
for i in range(finalDims.shape[0]):
plt.text(finalDims[i,0], finalDims[i,1], ntpath.basename(wavFilesList[i].replace('.wav','')), horizontalalignment='center', verticalalignment='center', fontsize=10)
plt.plot(finalDims[i,0], finalDims[i,1], '*r')
plt.xlim([1.2*finalDims[:,0].min(), 1.2*finalDims[:,0].max()])
plt.ylim([1.2*finalDims[:,1].min(), 1.2*finalDims[:,1].max()])
plt.show()
SM = 1.0 - distance.squareform(distance.pdist(finalDims2, 'cosine'))
for i in range(SM.shape[0]):
SM[i,i] = 0.0;
chordialDiagram("visualization", SM, 0.50, namesToVisualize, namesCategoryToVisualize)
SM = 1.0 - distance.squareform(distance.pdist(F, 'cosine'))
for i in range(SM.shape[0]):
SM[i,i] = 0.0;
chordialDiagram("visualizationInitial", SM, 0.50, namesToVisualize, namesCategoryToVisualize)
# plot super-categories (i.e. artistname
uNamesCategoryToVisualize = sort(list(set(namesCategoryToVisualize)))
finalDimsGroup = np.zeros( (len(uNamesCategoryToVisualize), finalDims2.shape[1] ) )
for i, uname in enumerate(uNamesCategoryToVisualize):
indices = [j for j, x in enumerate(namesCategoryToVisualize) if x == uname]
f = finalDims2[indices, :]
finalDimsGroup[i, :] = f.mean(axis=0)
SMgroup = 1.0 - distance.squareform(distance.pdist(finalDimsGroup, 'cosine'))
for i in range(SMgroup.shape[0]):
SMgroup[i,i] = 0.0;
chordialDiagram("visualizationGroup", SMgroup, 0.50, uNamesCategoryToVisualize, uNamesCategoryToVisualize)