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1 #!/usr/bin/env python
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2 # encoding: utf-8
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3 """
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4 SegEval.py
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5
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6 The main segmentation program.
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7
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8 Created by mi tian on 2015-04-02.
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9 Copyright (c) 2015 __MyCompanyName__. All rights reserved.
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10 """
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11
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12 # Load starndard python libs
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13 import sys, os, optparse, csv
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14 from itertools import combinations
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15 from os.path import join, isdir, isfile, abspath, dirname, basename, split, splitext
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16 from copy import copy
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17
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18 import matplotlib
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19 # matplotlib.use('Agg')
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20 import matplotlib.pyplot as plt
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21 import matplotlib.gridspec as gridspec
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22 import numpy as np
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23 import scipy as sp
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24 from scipy.signal import correlate2d, convolve2d, filtfilt, resample
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25 from scipy.ndimage.filters import *
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26 from sklearn.decomposition import PCA
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27 from sklearn.mixture import GMM
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28 from sklearn.cluster import KMeans
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29 from sklearn.preprocessing import normalize
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30 from sklearn.metrics.pairwise import pairwise_distances
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31
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32 # Load dependencies
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33 from utils.SegUtil import getMean, getStd, getDelta, getSSM, reduceSSM, upSample, normaliseFeature
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34 from utils.PeakPickerUtil import PeakPicker
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35 from utils.gmmdist import *
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36 from utils.GmmMetrics import GmmDistance
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37 from utils.RankClustering import rClustering
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38 from utils.kmeans import Kmeans
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39 from utils.PathTracker import PathTracker
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40
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41 # Load bourdary retrieval utilities
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42 import cnmf as cnmf_S
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43 import foote as foote_S
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44 import sf as sf_S
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45 import fmc2d as fmc2d_S
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46 import novelty as novelty_S
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47
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48 # Algorithm params
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49 h = 8 # Size of median filter for features in C-NMF
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50 R = 15 # Size of the median filter for the activation matrix C-NMF
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51 rank = 4 # Rank of decomposition for the boundaries
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52 rank_labels = 6 # Rank of decomposition for the labels
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53 R_labels = 6 # Size of the median filter for the labels
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54 # Foote
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55 M = 2 # Median filter for the audio features (in beats)
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56 Mg = 32 # Gaussian kernel size
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57 L = 16 # Size of the median filter for the adaptive threshold
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mitian@1
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58 # 2D-FMC
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59 N = 8 # Size of the fixed length segments (for 2D-FMC)
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60
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61
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62 # Define arg parser
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63 def parse_args():
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64 op = optparse.OptionParser()
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65 # IO options
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66 op.add_option('-g', '--gammatonegram-features', action="store", dest="GF", default='/Volumes/c4dm-03/people/mit/features/gammatonegram/qupujicheng/2048', type="str", help="Loading gammatone features from.." )
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67 op.add_option('-s', '--spectrogram-features', action="store", dest="SF", default='/Volumes/c4dm-03/people/mit/features/spectrogram/qupujicheng/2048', type="str", help="Loading spectral features from.." )
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68 op.add_option('-t', '--tempogram-features', action="store", dest="TF", default='/Volumes/c4dm-03/people/mit/features/tempogram/qupujicheng/tempo_features_6s', type="str", help="Loading tempogram features from.." )
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69 op.add_option('-f', '--featureset', action="store", dest="FEATURES", default='[0, 1, 2, 3]', type="str", help="Choose feature subsets (input a list of integers) used for segmentation -- gammtone, chroma, timbre, tempo -- 0, 1, 2, 3." )
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70 op.add_option('-a', '--annotations', action="store", dest="GT", default='/Volumes/c4dm-03/people/mit/annotation/qupujicheng/lowercase', type="str", help="Loading annotation files from.. ")
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71 op.add_option('-o', '--ouput', action="store", dest="OUTPUT", default='/Volumes/c4dm-03/people/mit/segmentation/gammatone/qupujicheng', type="str", help="Write segmentation results to ")
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72
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73 # boundary retrieval options
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74 op.add_option('-b', '--bounrary-method', action="store", dest="BOUNDARY", type='choice', choices=['novelty', 'cnmf', 'foote', 'sf'], default='novelty', help="Choose boundary retrieval algorithm ('novelty', 'cnmf', 'sf', 'fmc2d')." )
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75 op.add_option('-l', '--labeling-method', action="store", dest="LABEL", type='choice', choices=['cnmf', 'fmc2d'], default='cnmf', help="Choose boundary labeling algorithm ('cnmf', 'fmc2d')." )
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76
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77 # Plot/print/mode options
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78 op.add_option('-p', '--plot', action="store_true", dest="PLOT", default=False, help="Save plots")
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79 op.add_option('-e', '--test-mode', action="store_true", dest="TEST", default=False, help="Test mode")
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80 op.add_option('-v', '--verbose-mode', action="store_true", dest="VERBOSE", default=False, help="Print results in verbose mode.")
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81
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82 return op.parse_args()
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83 options, args = parse_args()
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84
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85 class FeatureObj() :
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86 __slots__ = ['key', 'audio', 'timestamps', 'gammatone_features', 'tempo_features', 'timbre_features', 'harmonic_features', 'gammatone_ssm', 'tempo_ssm', 'timbre_features', 'harmonic_ssm', 'ssm_timestamps']
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87
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88 class AudioObj():
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89 __slots__ = ['name', 'feature_list', 'gt', 'label', 'gammatone_features', 'tempo_features', 'timbre_features', 'harmonic_features', 'combined_features',\
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90 'gammatone_ssm', 'tempo_ssm', 'timbre_ssm', 'harmonic_ssm', 'combined_ssm', 'ssm', 'ssm_timestamps', 'tempo_timestamps']
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91
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92 class EvalObj():
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93 __slots__ = ['TP', 'FP', 'FN', 'P', 'R', 'F', 'AD', 'DA']
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94
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95
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96 class SSMseg(object):
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97 '''The main segmentation object'''
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98 def __init__(self):
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99 self.SampleRate = 44100
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100 self.NqHz = self.SampleRate/2
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101 self.timestamp = []
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102 self.previousSample = 0.0
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103 self.featureWindow = 6.0
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104 self.featureStep = 3.0
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105 self.kernel_size = 64 # Adjust this param according to the feature resolution.pq
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106 self.blockSize = 2048
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107 self.stepSize = 1024
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108
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109 '''NOTE: Match the following params with those used for feature extraction!'''
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110
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111 '''NOTE: Unlike spectrogram ones, Gammatone features are extracted without taking an FFT. The windowing is done under the purpose of chunking
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112 the audio to facilitate the gammatone filtering with the specified blockSize and stepSize. The resulting gammatonegram is aggregated every
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113 gammatoneLen without overlap.'''
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114 self.gammatoneLen = 2048
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115 self.gammatoneBandGroups = [0, 2, 6, 10, 13, 17, 20]
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116 self.nGammatoneBands = 20
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117 self.lowFreq = 100
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118 self.highFreq = self.SampleRate / 4
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119
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120 '''Settings for extracting tempogram features.'''
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121 self.tempoWindow = 6.0
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122 self.bpmBands = [30, 45, 60, 80, 100, 120, 180, 240, 400, 600]
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123
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124 '''Peak picking settings for novelty based method'''
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125 self.threshold = 30
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126 self.confidence_threshold = 0.5
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127 self.delta_threshold = 0.0
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128 self.backtracking_threshold = 1.9
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129 self.polyfitting_on = True
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130 self.medfilter_on = True
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131 self.LPfilter_on = True
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132 self.whitening_on = False
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133 self.aCoeffs = [1.0000, -0.5949, 0.2348]
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134 self.bCoeffs = [0.1600, 0.3200, 0.1600]
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135 self.cutoff = 0.34
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136 self.medianWin = 7
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137
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138
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139 def pairwiseF(self, annotation, detection, tolerance=3.0, combine=1.0, idx2time=None):
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140 '''Pairwise F measure evaluation of detection rates.'''
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141
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142 res = EvalObj()
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143 res.TP, res.FP, res.FN = 0, 0, 0
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144 res.P, res.R, res.F = 0.0, 0.0, 0.0
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145 res.AD, res.DA = 0.0, 0.0
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146
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147 if len(detection) == 0:
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148 return res
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149
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150 gt = len(annotation) # Total number of ground truth data points
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151 dt = len(detection) # Total number of experimental data points
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152 foundIdx = []
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153 D_AD = np.zeros(gt)
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154 D_DA = np.zeros(dt)
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155
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156 if idx2time != None:
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157 # Map detected idxs to real time
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158 detection = [idx2time[int(np.rint(i))] for i in detection] + [annotation[-1]]
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159 # print 'detection', detection
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160 detection = np.append(detection, annotation[-1])
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161
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162 for dtIdx in xrange(dt):
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163 D_DA[dtIdx] = np.min(abs(detection[dtIdx] - annotation))
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164 for gtIdx in xrange(gt):
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165 D_AD[gtIdx] = np.min(abs(annotation[gtIdx] - detection))
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166 for dtIdx in xrange(dt):
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167 if (annotation[gtIdx] >= detection[dtIdx] - tolerance/2.0) and (annotation[gtIdx] <= detection[dtIdx] + tolerance/2.0):
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168 res.TP = res.TP + 1.0
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169 foundIdx.append(gtIdx)
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170 foundIdx = list(set(foundIdx))
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171 res.TP = len(foundIdx)
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172 res.FP = max(0, dt - res.TP)
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173 res.FN = max(0, gt - res.TP)
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174
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175 res.AD = np.mean(D_AD)
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176 res.DA = np.mean(D_DA)
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177
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178
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179 if res.TP == 0:
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180 return res
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181
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182 res.P = res.TP / float(dt)
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183 res.R = res.TP / float(gt)
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184 res.F = 2 * res.P * res.R / (res.P + res.R)
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185 return res
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186
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187 def writeIndividualHeader(self, filename):
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188 '''Write header of output files for individual features.'''
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189
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190 with open(filename, 'a') as f:
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191 csvwriter = csv.writer(f, delimiter=',')
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192 csvwriter.writerow(['audio', 'gammatone_tp_05', 'gammatone_fp_05', 'gammatone_fn_05', 'gammatone_P_05', 'gammatone_R_05', 'gammatone_F_05', 'gammatone_AD_05', 'gammatone_DA_05', 'gammatone_tp_3', \
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193 'gammatone_fp_3', 'gammatone_fn_3', 'gammatone_P_3', 'gammatone_R_3', 'gammatone_F_3', 'gammatone_AD_3', 'gammatone_DA_3', 'harmonic_tp_05', 'harmonic_fp_05', 'harmonic_fn_05', 'harmonic_P_05', \
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194 'harmonic_R_05', 'harmonic_F_05', 'harmonic_AD_05', 'harmonic_DA_05', 'harmonic_tp_3', 'harmonic_fp_3', 'harmonic_fn_3', 'harmonic_P_3', 'harmonic_R_3', 'harmonic_F_3', 'harmonic_AD_3', 'harmonic_DA_3', \
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195 'timbre_tp_05', 'timbre_fp_05', 'timbre_fn_05', 'timbre_P_05', 'timbre_R_05', 'timbre_F_05', 'timbre_AD_05', 'timbre_DA_05', 'timbre_tp_3', 'timbre_fp_3', 'timbre_fn_3', 'timbre_P_3', 'timbre_R_3', \
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196 'timbre_F_3', 'timbre_AD_3', 'timbre_DA_3', 'tempo_tp_05', 'tempo_fp_05', 'tempo_fn_05', 'tempo_P_05', 'tempo_R_05', 'tempo_F_05', 'tempo_AD_05', 'tempo_DA_05', \
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197 'tempo_tp_3', 'tempo_fp_3', 'tempo_fn_3', 'tempo_P_3', 'tempo_R_3', 'tempo_F_3', 'tempo_AD_3', 'tempo_DA_3'])
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198
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199 def writeIndividualRes(self, filename, ao_name, gt_res_05, gt_res_3, harmonic_res_05, harmonic_res_3, timbre_res_05, timbre_res_3, tempo_res_05, tempo_res_3):
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200 '''Write result of single detection for individual features.'''
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201
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202 with open(filename, 'a') as f:
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203 csvwriter = csv.writer(f, delimiter=',')
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204 csvwriter.writerow([ao_name, gt_res_05.TP, gt_res_05.FP, gt_res_05.FN, gt_res_05.P, gt_res_05.R, gt_res_05.F, gt_res_05.AD, gt_res_05.DA, gt_res_3.TP, gt_res_3.FP, gt_res_3.FN, gt_res_3.P, \
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205 gt_res_3.R, gt_res_3.F, gt_res_3.AD, gt_res_3.DA, harmonic_res_05.TP, harmonic_res_05.FP, harmonic_res_05.FN, harmonic_res_05.P, harmonic_res_05.R, harmonic_res_05.F, harmonic_res_05.AD, harmonic_res_05.DA, \
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206 harmonic_res_3.TP, harmonic_res_3.FP, harmonic_res_3.FN, harmonic_res_3.P, harmonic_res_3.R, harmonic_res_3.F, harmonic_res_3.AD, harmonic_res_3.DA, timbre_res_05.TP, timbre_res_05.FP, \
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207 timbre_res_05.FN, timbre_res_05.P, timbre_res_05.R, timbre_res_05.F, timbre_res_05.AD, timbre_res_05.DA, timbre_res_3.TP, timbre_res_3.FP, timbre_res_3.FN, timbre_res_3.P, timbre_res_3.R, timbre_res_3.F, \
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208 timbre_res_3.AD, timbre_res_3.DA, tempo_res_05.TP, tempo_res_05.FP, tempo_res_05.FN, tempo_res_05.P, tempo_res_05.R, tempo_res_05.F, tempo_res_05.AD, tempo_res_05.DA, tempo_res_3.TP, tempo_res_3.FP, \
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209 tempo_res_3.FN, tempo_res_3.P, tempo_res_3.R, tempo_res_3.F, tempo_res_3.AD, tempo_res_3.DA])
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210
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211 def writeCombinedHeader(self, filename):
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212 '''Write header of output files for combined features.'''
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213
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214 with open(filename, 'a') as f:
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215 csvwriter = csv.writer(f, delimiter=',')
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216 csvwriter.writerow(['audio', 'gt_tb_P_0.5', 'gt_tb_R_0.5', 'gt_tb_F_0.5', 'gt_tb_P_3', 'gt_tb_R_3', 'gt_tb_F_3', 'gt_tp_P_0.5', 'gt_tp_R_0.5', 'gt_tp_F_0.5', 'gt_tp_P_3', 'gt_tp_R_3', 'gt_tp_F_3',\
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217 'gt_hm_P_0.5', 'gt_hm_R_0.5', 'gt_hm_F_0.5', 'gt_hm_P_3', 'gt_hm_R_3', 'gt_hm_F_3', 'tb_tp_P_0.5', 'tb_tp_R_0.5', 'tb_tp_F_0.5', 'tb_tp_P_3', 'tb_tp_R_3', 'tb_tp_F_3', 'hm_tb_P_0.5', 'hm_tb_R_0.5', 'hm_tb_F_0.5', \
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218 'hm_tb_P_3', 'hm_tb_R_3', 'hm_tb_F_3', 'hm_tp_P_0.5', 'hm_tp_R_0.5', 'hm_tp_F_0.5', 'hm_tp_P_3', 'hm_tp_R_3', 'hm_tp_F_3', 'gt_tb_tp_P_0.5', 'gt_tb_tp_R_0.5', 'gt_tb_tp_F_0.5', 'gt_tb_tp_P_3', 'gt_tb_tp_R_3', \
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219 'gt_tb_tp_F_3', 'gt_hm_tb_P_0.5', 'gt_hm_tb_R_0.5', 'gt_hm_tb_F_0.5', 'gt_hm_tb_P_3', 'gt_hm_tb_R_3', 'gt_hm_tb_F_3', 'gt_hm_tp_P_0.5', 'gt_hm_tp_R_0.5', 'gt_hm_tp_F_0.5', 'gt_hm_tp_P_3', 'gt_hm_tp_R_3', 'gt_hm_tp_F_3', \
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220 'hm_tb_tp_P_0.5', 'hm_tb_tp_R_0.5', 'hm_tb_tp_F_0.5', 'hm_tb_tp_P_3', 'hm_tb_tp_R_3', 'hm_tb_tp_F_3', 'gt_hm_tb_tp_P_0.5', 'gt_hm_tb_tp_R_0.5', 'gt_hm_tb_tp_F_0.5', 'gt_hm_tb_tp_P_3', 'gt_hm_tb_tp_R_3', 'gt_hm_tb_tp_F_3'])
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mitian@3
|
221
|
|
mitian@4
|
222 def writeCombinedRes(self, filename, ao_name, gt_hm_res_05, gt_hm_res_3, gt_tb_res_05, gt_tb_res_3, gt_tp_res_05, gt_tp_res_3, hm_tb_res_05, hm_tb_res_3, hm_tp_res_05, hm_tp_res_3, \
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|
mitian@4
|
223 tb_tp_res_05, tb_tp_res_3, gt_hm_tb_res_05, gt_hm_tb_res_3, gt_hm_tp_res_05, gt_hm_tp_res_3, gt_tb_tp_res_05, gt_tb_tp_res_3, hm_tb_tp_res_05, hm_tb_tp_res_3, gt_hm_tb_tp_res_05, gt_hm_tb_tp_res_3):
|
|
mitian@3
|
224 '''Write result of single detection for combined features.'''
|
|
mitian@3
|
225
|
|
mitian@3
|
226 with open(filename, 'a') as f:
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|
mitian@3
|
227 csvwriter = csv.writer(f, delimiter=',')
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|
mitian@3
|
228 csvwriter.writerow([ao_name, gt_tb_res_05.P, gt_tb_res_05.R, gt_tb_res_05.F, gt_tb_res_3.P, gt_tb_res_3.R, gt_tb_res_3.F, gt_tp_res_05.P, gt_tp_res_05.R, gt_tp_res_05.F, gt_tp_res_3.P, gt_tp_res_3.R, gt_tp_res_3.F, \
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|
mitian@3
|
229 gt_hm_res_05.P, gt_hm_res_05.R, gt_hm_res_05.F, gt_hm_res_3.P, gt_hm_res_3.R, gt_hm_res_3.F, tb_tp_res_05.P, tb_tp_res_05.R, tb_tp_res_05.F, tb_tp_res_3.P, tb_tp_res_3.R, tb_tp_res_3.F, \
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|
mitian@4
|
230 hm_tb_res_05.P, hm_tb_res_05.R, hm_tb_res_05.F, hm_tb_res_3.P, hm_tb_res_3.R, hm_tb_res_3.F, hm_tp_res_05.P, hm_tp_res_05.R, hm_tp_res_05.F, hm_tp_res_3.P, hm_tp_res_3.R, hm_tp_res_3.F, \
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|
mitian@4
|
231 gt_tb_tp_res_05.P, gt_tb_tp_res_05.R, gt_tb_tp_res_05.F, gt_tb_tp_res_3.P, gt_tb_tp_res_3.R, gt_tb_tp_res_3.F, gt_hm_tb_res_05.P, gt_hm_tb_res_05.R, gt_hm_tb_res_05.F, gt_hm_tb_res_3.P, gt_hm_tb_res_3.R, gt_hm_tb_res_3.F, \
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|
mitian@4
|
232 gt_hm_tp_res_05.P, gt_hm_tp_res_05.R, gt_hm_tp_res_05.F, gt_hm_tp_res_3.P, gt_hm_tp_res_3.R, gt_hm_tp_res_3.F, hm_tb_tp_res_05.P, hm_tb_tp_res_05.R, hm_tb_tp_res_05.F, hm_tb_tp_res_3.P, hm_tb_tp_res_3.R, hm_tb_tp_res_3.F, \
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|
mitian@4
|
233 gt_hm_tb_tp_res_05.P, gt_hm_tb_tp_res_05.R, gt_hm_tb_tp_res_05.F, gt_hm_tb_tp_res_3.P, gt_hm_tb_tp_res_3.R, gt_hm_tb_tp_res_3.F])
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|
mitian@3
|
234
|
|
mitian@4
|
235 def writeMergedHeader(self, filename):
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mitian@4
|
236 '''Write header of output files merging individual detections.'''
|
|
mitian@4
|
237 with open(filename, 'a') as f:
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|
mitian@4
|
238 csvwriter = csv.writer(f, delimiter=',')
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|
mitian@4
|
239 csvwriter.writerow(['audio', 'merged_tp_05', 'merged_fp_05', 'merged_fn_05', 'merged_P_05', 'merged_R_05', 'merged_F_05', 'merged_AD_05', 'merged_DA_05', 'merged_tp_3', \
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|
mitian@4
|
240 'merged_fp_3', 'merged_fn_3', 'merged_P_3', 'merged_R_3', 'merged_F_3', 'merged_AD_3', 'merged_DA_3'])
|
|
mitian@4
|
241
|
|
mitian@4
|
242 def writeMergedRes(self, filename, ao_name, merged_res_05, merged_res_3):
|
|
mitian@4
|
243 '''Write results by merging individual detections.'''
|
|
mitian@4
|
244 with open(filename, 'a') as f:
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|
mitian@4
|
245 csvwriter = csv.writer(f, delimiter=',')
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|
mitian@4
|
246 csvwriter.writerow([ao_name, merged_res_05.TP, merged_res_05.FP, merged_res_05.FN, merged_res_05.P, merged_res_05.R, merged_res_05.F, merged_res_05.AD, merged_res_05.DA, \
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|
mitian@4
|
247 merged_res_3.TP, merged_res_3.FP, merged_res_3.FN, merged_res_3.P, merged_res_3.R, merged_res_3.F, merged_res_3.AD, merged_res_3.DA])
|
|
mitian@4
|
248
|
|
mitian@4
|
249
|
|
mi@0
|
250 def process(self):
|
|
mi@0
|
251 '''For the aggregated input features, discard a propertion each time as the pairwise distances within the feature space descending.
|
|
mi@0
|
252 In the meanwhile evaluate the segmentation result and track the trend of perfomance changing by measuring the feature selection
|
|
mi@0
|
253 threshold - segmentation f measure curve.
|
|
mi@0
|
254 '''
|
|
mi@0
|
255
|
|
mi@0
|
256 peak_picker = PeakPicker()
|
|
mi@0
|
257 peak_picker.params.alpha = 9.0 # Alpha norm
|
|
mi@0
|
258 peak_picker.params.delta = self.delta_threshold # Adaptive thresholding delta
|
|
mi@0
|
259 peak_picker.params.QuadThresh_a = (100 - self.threshold) / 1000.0
|
|
mi@0
|
260 peak_picker.params.QuadThresh_b = 0.0
|
|
mi@0
|
261 peak_picker.params.QuadThresh_c = (100 - self.threshold) / 1500.0
|
|
mi@0
|
262 peak_picker.params.rawSensitivity = 20
|
|
mi@0
|
263 peak_picker.params.aCoeffs = self.aCoeffs
|
|
mi@0
|
264 peak_picker.params.bCoeffs = self.bCoeffs
|
|
mi@0
|
265 peak_picker.params.preWin = self.medianWin
|
|
mi@0
|
266 peak_picker.params.postWin = self.medianWin + 1
|
|
mi@0
|
267 peak_picker.params.LP_on = self.LPfilter_on
|
|
mi@0
|
268 peak_picker.params.Medfilt_on = self.medfilter_on
|
|
mi@0
|
269 peak_picker.params.Polyfit_on = self.polyfitting_on
|
|
mi@0
|
270 peak_picker.params.isMedianPositive = False
|
|
mi@0
|
271
|
|
mi@0
|
272 # Settings used for feature extraction
|
|
mi@0
|
273 feature_window_frame = int(self.SampleRate / self.gammatoneLen * self.featureWindow)
|
|
mi@0
|
274 feature_step_frame = int(0.5 * self.SampleRate / self.gammatoneLen * self.featureStep)
|
|
mi@0
|
275 aggregation_window, aggregation_step = 100, 50
|
|
mi@0
|
276 featureRate = float(self.SampleRate) / self.stepSize
|
|
mi@0
|
277
|
|
mi@0
|
278 audio_files = [x for x in os.listdir(options.GT) if not x.startswith(".") ]
|
|
mitian@4
|
279 if options.TEST:
|
|
mitian@4
|
280 audio_files = audio_files[:1]
|
|
mi@0
|
281 audio_files.sort()
|
|
mi@0
|
282 audio_list = []
|
|
mi@0
|
283
|
|
mi@0
|
284 gammatone_feature_list = [i for i in os.listdir(options.GF) if not i.startswith('.')]
|
|
mitian@4
|
285 gammatone_feature_list = ['contrast6', 'rolloff4', 'dct']
|
|
mi@0
|
286 tempo_feature_list = [i for i in os.listdir(options.TF) if not i.startswith('.')]
|
|
mitian@4
|
287 tempo_feature_list = ['ti', 'tir']
|
|
mitian@4
|
288 timbre_feature_list = ['mfcc_harmonic']
|
|
mitian@4
|
289 harmonic_feature_list = ['chromagram']
|
|
mi@0
|
290
|
|
mi@0
|
291 gammatone_feature_list = [join(options.GF, f) for f in gammatone_feature_list]
|
|
mi@0
|
292 timbre_feature_list = [join(options.SF, f) for f in timbre_feature_list]
|
|
mi@0
|
293 tempo_feature_list = [join(options.TF, f) for f in tempo_feature_list]
|
|
mi@0
|
294 harmonic_feature_list = [join(options.SF, f) for f in harmonic_feature_list]
|
|
mi@0
|
295
|
|
mi@0
|
296 fobj_list = []
|
|
mi@0
|
297
|
|
mi@0
|
298 # For each audio file, load specific features
|
|
mi@0
|
299 for audio in audio_files:
|
|
mi@0
|
300 ao = AudioObj()
|
|
mi@0
|
301 ao.name = splitext(audio)[0]
|
|
mitian@5
|
302 annotation_file = join(options.GT, ao.name+'.txt') # iso, salami
|
|
mitian@5
|
303 ao.gt = np.genfromtxt(annotation_file, usecols=0)
|
|
mitian@5
|
304 ao.label = np.genfromtxt(annotation_file, usecols=1, dtype=str)
|
|
mitian@5
|
305 # annotation_file = join(options.GT, ao.name+'.csv') # qupujicheng
|
|
mitian@5
|
306 # ao.gt = np.genfromtxt(annotation_file, usecols=0, delimiter=',')
|
|
mitian@5
|
307 # ao.label = np.genfromtxt(annotation_file, usecols=1, delimiter=',', dtype=str)
|
|
mi@0
|
308
|
|
mi@0
|
309 gammatone_featureset, timbre_featureset, tempo_featureset, harmonic_featureset = [], [], [], []
|
|
mi@0
|
310 for feature in gammatone_feature_list:
|
|
mi@0
|
311 for f in os.listdir(feature):
|
|
mi@0
|
312 if f[:f.find('_vamp')]==ao.name:
|
|
mi@0
|
313 gammatone_featureset.append(np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[:,1:])
|
|
mi@0
|
314 break
|
|
mi@0
|
315 if len(gammatone_feature_list) > 1:
|
|
mi@0
|
316 n_frame = np.min([x.shape[0] for x in gammatone_featureset])
|
|
mi@0
|
317 gammatone_featureset = [x[:n_frame,:] for x in gammatone_featureset]
|
|
mi@0
|
318 ao.gammatone_features = np.hstack((gammatone_featureset))
|
|
mi@0
|
319 else:
|
|
mi@0
|
320 ao.gammatone_features = gammatone_featureset[0]
|
|
mi@0
|
321
|
|
mi@0
|
322 for feature in timbre_feature_list:
|
|
mi@0
|
323 for f in os.listdir(feature):
|
|
mi@0
|
324 if f[:f.find('_vamp')]==ao.name:
|
|
mi@0
|
325 timbre_featureset.append(np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[:,1:])
|
|
mi@0
|
326 break
|
|
mi@0
|
327 if len(timbre_feature_list) > 1:
|
|
mi@0
|
328 n_frame = np.min([x.shape[0] for x in timbre_featureset])
|
|
mi@0
|
329 timbre_featureset = [x[:n_frame,:] for x in timbre_featureset]
|
|
mi@0
|
330 ao.timbre_features = np.hstack((timbre_featureset))
|
|
mi@0
|
331 else:
|
|
mi@0
|
332 ao.timbre_features = timbre_featureset[0]
|
|
mi@0
|
333 for feature in tempo_feature_list:
|
|
mi@0
|
334 for f in os.listdir(feature):
|
|
mi@0
|
335 if f[:f.find('_vamp')]==ao.name:
|
|
mi@0
|
336 tempo_featureset.append(np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[1:,1:])
|
|
mi@0
|
337 ao.tempo_timestamps = np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[1:,0]
|
|
mi@0
|
338 break
|
|
mi@0
|
339 if len(tempo_feature_list) > 1:
|
|
mi@0
|
340 n_frame = np.min([x.shape[0] for x in tempo_featureset])
|
|
mi@0
|
341 tempo_featureset = [x[:n_frame,:] for x in tempo_featureset]
|
|
mi@0
|
342 ao.tempo_features = np.hstack((tempo_featureset))
|
|
mi@0
|
343 else:
|
|
mi@0
|
344 ao.tempo_features = tempo_featureset[0]
|
|
mi@0
|
345 for feature in harmonic_feature_list:
|
|
mi@0
|
346 for f in os.listdir(feature):
|
|
mi@0
|
347 if f[:f.find('_vamp')]==ao.name:
|
|
mi@0
|
348 harmonic_featureset.append(np.genfromtxt(join(feature, f), delimiter=',',filling_values=0.0)[:,1:])
|
|
mi@0
|
349 break
|
|
mi@0
|
350 if len(harmonic_feature_list) > 1:
|
|
mi@0
|
351 n_frame = np.min([x.shape[0] for x in harmonic_featureset])
|
|
mi@0
|
352 harmonic_featureset = [x[:n_frame,:] for x in harmonic_featureset]
|
|
mi@0
|
353 ao.harmonic_features = np.hstack((harmonic_featureset))
|
|
mi@0
|
354 else:
|
|
mi@0
|
355 ao.harmonic_features = harmonic_featureset[0]
|
|
mi@0
|
356
|
|
mi@0
|
357 # Get aggregated features for computing ssm
|
|
mi@0
|
358 aggregation_window, aggregation_step = 1,1
|
|
mi@0
|
359 featureRate = float(self.SampleRate) /self.stepSize
|
|
mi@0
|
360 pca = PCA(n_components=5)
|
|
mi@0
|
361
|
|
mi@0
|
362 # Resample and normalise features
|
|
mitian@4
|
363 step = ao.tempo_features.shape[0]
|
|
mi@0
|
364 ao.gammatone_features = resample(ao.gammatone_features, step)
|
|
mi@0
|
365 ao.gammatone_features = normaliseFeature(ao.gammatone_features)
|
|
mi@0
|
366 ao.timbre_features = resample(ao.timbre_features, step)
|
|
mi@0
|
367 ao.timbre_features = normaliseFeature(ao.timbre_features)
|
|
mi@0
|
368 ao.harmonic_features = resample(ao.harmonic_features, step)
|
|
mi@0
|
369 ao.harmonic_features = normaliseFeature(ao.harmonic_features)
|
|
mitian@4
|
370 ao.tempo_features = normaliseFeature(ao.tempo_features)
|
|
mi@0
|
371
|
|
mi@0
|
372 pca.fit(ao.gammatone_features)
|
|
mi@0
|
373 ao.gammatone_features = pca.transform(ao.gammatone_features)
|
|
mi@0
|
374 ao.gammatone_ssm = getSSM(ao.gammatone_features)
|
|
mi@0
|
375
|
|
mi@0
|
376 pca.fit(ao.tempo_features)
|
|
mi@0
|
377 ao.tempo_features = pca.transform(ao.tempo_features)
|
|
mi@0
|
378 ao.tempo_ssm = getSSM(ao.tempo_features)
|
|
mi@0
|
379
|
|
mi@0
|
380 pca.fit(ao.timbre_features)
|
|
mi@0
|
381 ao.timbre_features = pca.transform(ao.timbre_features)
|
|
mi@0
|
382 ao.timbre_ssm = getSSM(ao.timbre_features)
|
|
mi@0
|
383
|
|
mi@0
|
384 pca.fit(ao.harmonic_features)
|
|
mi@0
|
385 ao.harmonic_features = pca.transform(ao.harmonic_features)
|
|
mi@0
|
386 ao.harmonic_ssm = getSSM(ao.harmonic_features)
|
|
mi@0
|
387
|
|
mi@0
|
388 ao.ssm_timestamps = np.array(map(lambda x: ao.tempo_timestamps[aggregation_step*x], np.arange(0, ao.gammatone_ssm.shape[0])))
|
|
mi@0
|
389
|
|
mi@0
|
390 audio_list.append(ao)
|
|
mi@0
|
391
|
|
mitian@3
|
392 # Prepare output files.
|
|
mitian@3
|
393 outfile1 = join(options.OUTPUT, 'individual_novelty.csv')
|
|
mitian@3
|
394 outfile2 = join(options.OUTPUT, 'individual_foote.csv')
|
|
mitian@3
|
395 outfile3 = join(options.OUTPUT, 'individual_sf.csv')
|
|
mitian@3
|
396 outfile4 = join(options.OUTPUT, 'individual_cnmf.csv')
|
|
mitian@3
|
397
|
|
mitian@3
|
398 outfile5 = join(options.OUTPUT, 'combined_novelty.csv')
|
|
mitian@3
|
399 outfile6 = join(options.OUTPUT, 'combined_foote.csv')
|
|
mitian@3
|
400 outfile7 = join(options.OUTPUT, 'combined_sf.csv')
|
|
mitian@3
|
401 outfile8 = join(options.OUTPUT, 'combined_cnmf.csv')
|
|
mitian@3
|
402
|
|
mitian@4
|
403 outfile9 = join(options.OUTPUT, 'individual_merged.csv')
|
|
mitian@4
|
404
|
|
mitian@4
|
405 self.writeIndividualHeader(outfile1)
|
|
mitian@4
|
406 self.writeIndividualHeader(outfile2)
|
|
mitian@4
|
407 self.writeIndividualHeader(outfile3)
|
|
mitian@4
|
408 self.writeIndividualHeader(outfile4)
|
|
mitian@3
|
409
|
|
mitian@4
|
410 # self.writeCombinedHeader(outfile5)
|
|
mitian@4
|
411 # self.writeCombinedHeader(outfile6)
|
|
mitian@4
|
412 self.writeCombinedHeader(outfile7)
|
|
mitian@4
|
413 self.writeCombinedHeader(outfile8)
|
|
mitian@4
|
414
|
|
mitian@4
|
415 self.writeMergedHeader(outfile9)
|
|
mitian@3
|
416
|
|
mi@0
|
417 print 'Segmenting using %s method' %options.BOUNDARY
|
|
mi@0
|
418 for i,ao in enumerate(audio_list):
|
|
mi@0
|
419 print 'processing: %s' %ao.name
|
|
mitian@3
|
420
|
|
mitian@3
|
421 ############################################################################################################################################
|
|
mitian@3
|
422 # Experiment 1: segmentation using individual features.
|
|
mitian@3
|
423
|
|
mitian@3
|
424 gammatone_novelty, smoothed_gammatone_novelty, gammatone_novelty_idxs = novelty_S.process(ao.gammatone_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
425 timbre_novelty, smoothed_timbre_novelty, timbre_novelty_idxs = novelty_S.process(ao.timbre_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
426 tempo_novelty, smoothed_harmonic_novelty, tempo_novelty_idxs = novelty_S.process(ao.tempo_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
427 harmonic_novelty, smoothed_tempo_novelty, harmonic_novelty_idxs = novelty_S.process(ao.harmonic_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
428
|
|
mitian@3
|
429 gammatone_cnmf_idxs = cnmf_S.segmentation(ao.gammatone_features, rank=rank, R=R, h=h, niter=300)
|
|
mitian@3
|
430 timbre_cnmf_idxs = cnmf_S.segmentation(ao.timbre_features, rank=rank, R=R, h=h, niter=300)
|
|
mitian@3
|
431 tempo_cnmf_idxs = cnmf_S.segmentation(ao.tempo_features, rank=rank, R=R, h=h, niter=300)
|
|
mitian@3
|
432 harmonic_cnmf_idxs = cnmf_S.segmentation(ao.harmonic_features, rank=rank, R=R, h=h, niter=300)
|
|
mitian@3
|
433
|
|
mitian@3
|
434 gammatone_foote_idxs = foote_S.segmentation(ao.gammatone_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
435 timbre_foote_idxs = foote_S.segmentation(ao.timbre_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
436 tempo_foote_idxs = foote_S.segmentation(ao.tempo_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
437 harmonic_foote_idxs = foote_S.segmentation(ao.harmonic_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
438
|
|
mitian@3
|
439 gammatone_sf_idxs = sf_S.segmentation(ao.gammatone_features)
|
|
mitian@3
|
440 timbre_sf_idxs = sf_S.segmentation(ao.timbre_features)
|
|
mitian@3
|
441 tempo_sf_idxs = sf_S.segmentation(ao.tempo_features)
|
|
mitian@3
|
442 harmonic_sf_idxs = sf_S.segmentation(ao.harmonic_features)
|
|
mitian@1
|
443
|
|
mitian@3
|
444 # Evaluate and write results.
|
|
mitian@3
|
445 gt_novelty_05 = self.pairwiseF(ao.gt, gammatone_novelty_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
446 gt_novelty_3 = self.pairwiseF(ao.gt, gammatone_novelty_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
447 harmonic_novelty_05 = self.pairwiseF(ao.gt, harmonic_novelty_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
448 harmonic_novelty_3 = self.pairwiseF(ao.gt, harmonic_novelty_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
449 tempo_novelty_05 = self.pairwiseF(ao.gt, tempo_novelty_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
450 tempo_novelty_3 = self.pairwiseF(ao.gt, tempo_novelty_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
451 timbre_novelty_05 = self.pairwiseF(ao.gt, timbre_novelty_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
452 timbre_novelty_3 = self.pairwiseF(ao.gt, timbre_novelty_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mi@0
|
453
|
|
mitian@3
|
454 gt_cnmf_05 = self.pairwiseF(ao.gt, gammatone_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
455 gt_cnmf_3 = self.pairwiseF(ao.gt, gammatone_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
456 harmonic_cnmf_05 = self.pairwiseF(ao.gt, harmonic_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
457 harmonic_cnmf_3 = self.pairwiseF(ao.gt, harmonic_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
458 tempo_cnmf_05 = self.pairwiseF(ao.gt, tempo_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
459 tempo_cnmf_3 = self.pairwiseF(ao.gt, tempo_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
460 timbre_cnmf_05 = self.pairwiseF(ao.gt, timbre_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
461 timbre_cnmf_3 = self.pairwiseF(ao.gt, timbre_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
462
|
|
mitian@3
|
463 gt_sf_05 = self.pairwiseF(ao.gt, gammatone_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
464 gt_sf_3 = self.pairwiseF(ao.gt, gammatone_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
465 harmonic_sf_05 = self.pairwiseF(ao.gt, harmonic_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
466 harmonic_sf_3 = self.pairwiseF(ao.gt, harmonic_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
467 tempo_sf_05 = self.pairwiseF(ao.gt, tempo_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
468 tempo_sf_3 = self.pairwiseF(ao.gt, tempo_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
469 timbre_sf_05 = self.pairwiseF(ao.gt, timbre_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
470 timbre_sf_3 = self.pairwiseF(ao.gt, timbre_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
471
|
|
mitian@3
|
472 gt_foote_05 = self.pairwiseF(ao.gt, gammatone_foote_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
473 gt_foote_3 = self.pairwiseF(ao.gt, gammatone_foote_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
474 harmonic_foote_05 = self.pairwiseF(ao.gt, harmonic_foote_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
475 harmonic_foote_3 = self.pairwiseF(ao.gt, harmonic_foote_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
476 tempo_foote_05 = self.pairwiseF(ao.gt, tempo_foote_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
477 tempo_foote_3 = self.pairwiseF(ao.gt, tempo_foote_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
478 timbre_foote_05 = self.pairwiseF(ao.gt, timbre_foote_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
479 timbre_foote_3 = self.pairwiseF(ao.gt, timbre_foote_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@1
|
480
|
|
mitian@5
|
481 self.writeIndividualRes(outfile1, ao.name, gt_novelty_05, gt_novelty_3, harmonic_novelty_05, harmonic_novelty_3, tempo_novelty_05, tempo_novelty_3, timbre_novelty_05, timbre_novelty_3)
|
|
mitian@5
|
482 self.writeIndividualRes(outfile2, ao.name, gt_cnmf_05, gt_cnmf_3, harmonic_cnmf_05, harmonic_cnmf_3, tempo_cnmf_05, tempo_cnmf_3, timbre_cnmf_05, timbre_cnmf_3)
|
|
mitian@5
|
483 self.writeIndividualRes(outfile3, ao.name, gt_sf_05, gt_sf_3, harmonic_sf_05, harmonic_sf_3, tempo_sf_05, tempo_sf_3, timbre_sf_05, timbre_sf_3)
|
|
mitian@5
|
484 self.writeIndividualRes(outfile4, ao.name, gt_foote_05, gt_foote_3, harmonic_foote_05, harmonic_foote_3, tempo_foote_05, tempo_foote_3, timbre_foote_05, timbre_foote_3)
|
|
mitian@1
|
485
|
|
mitian@1
|
486
|
|
mitian@3
|
487 ############################################################################################################################################
|
|
mitian@3
|
488 # Experiment 2: segmentation using combined features.
|
|
mi@2
|
489
|
|
mitian@3
|
490 # Dumping features.
|
|
mitian@3
|
491 gt_hm = np.hstack([ao.gammatone_features, ao.harmonic_features])
|
|
mitian@3
|
492 gt_tb = np.hstack([ao.gammatone_features, ao.timbre_features])
|
|
mitian@3
|
493 gt_tp = np.hstack([ao.gammatone_features, ao.tempo_features])
|
|
mitian@3
|
494 hm_tb = np.hstack([ao.harmonic_features, ao.timbre_features])
|
|
mitian@3
|
495 hm_tp = np.hstack([ao.harmonic_features, ao.tempo_features])
|
|
mitian@3
|
496 tb_tp = np.hstack([ao.timbre_features, ao.tempo_features])
|
|
mitian@4
|
497 gt_hm_tb = np.hstack([ao.gammatone_features, ao.harmonic_features, ao.timbre_features])
|
|
mitian@3
|
498 gt_hm_tp = np.hstack([ao.gammatone_features, ao.harmonic_features, ao.tempo_features])
|
|
mitian@3
|
499 gt_tb_tp = np.hstack([ao.gammatone_features, ao.timbre_features, ao.tempo_features])
|
|
mitian@3
|
500 hm_tb_tp = np.hstack([ao.harmonic_features, ao.timbre_features, ao.tempo_features])
|
|
mitian@3
|
501 gt_hm_tb_tp = np.hstack([ao.gammatone_features, ao.harmonic_features, ao.timbre_features, ao.tempo_features])
|
|
mitian@3
|
502
|
|
mitian@4
|
503 # Evaluting and writing results.
|
|
mitian@3
|
504 gt_hm_sf_idxs = sf_S.segmentation(gt_hm)
|
|
mitian@3
|
505 gt_tb_sf_idxs = sf_S.segmentation(gt_tb)
|
|
mitian@3
|
506 gt_tp_sf_idxs = sf_S.segmentation(gt_tp)
|
|
mitian@3
|
507 hm_tb_sf_idxs = sf_S.segmentation(hm_tb)
|
|
mitian@3
|
508 hm_tp_sf_idxs = sf_S.segmentation(hm_tp)
|
|
mitian@3
|
509 tb_tp_sf_idxs = sf_S.segmentation(tb_tp)
|
|
mitian@3
|
510 gt_hm_tb_sf_idxs = sf_S.segmentation(gt_hm_tb)
|
|
mitian@3
|
511 gt_hm_tp_sf_idxs = sf_S.segmentation(gt_hm_tp)
|
|
mitian@3
|
512 gt_tb_tp_sf_idxs = sf_S.segmentation(gt_tb_tp)
|
|
mitian@3
|
513 hm_tb_tp_sf_idxs = sf_S.segmentation(hm_tb_tp)
|
|
mitian@3
|
514 gt_hm_tb_tp_sf_idxs = sf_S.segmentation(gt_hm_tb_tp)
|
|
mitian@3
|
515
|
|
mitian@4
|
516 gt_hm_cnmf_idxs = cnmf_S.segmentation(gt_hm, rank=4, R=R, h=h, niter=300)
|
|
mitian@4
|
517 gt_tb_cnmf_idxs = cnmf_S.segmentation(gt_tb, rank=4, R=R, h=h, niter=300)
|
|
mitian@4
|
518 gt_tp_cnmf_idxs = cnmf_S.segmentation(gt_tp, rank=4, R=R, h=h, niter=300)
|
|
mitian@4
|
519 hm_tb_cnmf_idxs = cnmf_S.segmentation(hm_tb, rank=4, R=R, h=h, niter=300)
|
|
mitian@4
|
520 hm_tp_cnmf_idxs = cnmf_S.segmentation(hm_tp, rank=4, R=R, h=h, niter=300)
|
|
mitian@4
|
521 tb_tp_cnmf_idxs = cnmf_S.segmentation(tb_tp, rank=4, R=R, h=h, niter=300)
|
|
mitian@4
|
522 gt_hm_tb_cnmf_idxs = cnmf_S.segmentation(gt_hm_tb, rank=6, R=R, h=h, niter=300)
|
|
mitian@4
|
523 gt_hm_tp_cnmf_idxs = cnmf_S.segmentation(gt_hm_tp, rank=6, R=R, h=h, niter=300)
|
|
mitian@4
|
524 gt_tb_tp_cnmf_idxs = cnmf_S.segmentation(gt_tb_tp, rank=6, R=R, h=h, niter=300)
|
|
mitian@4
|
525 hm_tb_tp_cnmf_idxs = cnmf_S.segmentation(hm_tb_tp, rank=6, R=R, h=h, niter=300)
|
|
mitian@4
|
526 gt_hm_tb_tp_cnmf_idxs = cnmf_S.segmentation(gt_hm_tb_tp, rank=8, R=R, h=h, niter=300)
|
|
mitian@3
|
527
|
|
mitian@4
|
528 gt_hm_sf_05 = self.pairwiseF(ao.gt, gt_hm_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
529 gt_tb_sf_05 = self.pairwiseF(ao.gt, gt_tb_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
530 gt_tp_sf_05 = self.pairwiseF(ao.gt, gt_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
531 hm_tb_sf_05 = self.pairwiseF(ao.gt, hm_tb_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
532 hm_tp_sf_05 = self.pairwiseF(ao.gt, hm_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
533 tb_tp_sf_05 = self.pairwiseF(ao.gt, tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
534 gt_hm_tb_sf_05 = self.pairwiseF(ao.gt, gt_hm_tb_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
535 gt_hm_tp_sf_05 = self.pairwiseF(ao.gt, gt_hm_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
536 gt_tb_tp_sf_05 = self.pairwiseF(ao.gt, gt_tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
537 hm_tb_tp_sf_05 = self.pairwiseF(ao.gt, hm_tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
538 gt_hm_tb_tp_sf_05 = self.pairwiseF(ao.gt, gt_hm_tb_tp_sf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
539
|
|
mitian@4
|
540 gt_hm_sf_3 = self.pairwiseF(ao.gt, gt_hm_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
541 gt_tb_sf_3 = self.pairwiseF(ao.gt, gt_tb_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
542 gt_tp_sf_3 = self.pairwiseF(ao.gt, gt_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
543 hm_tb_sf_3 = self.pairwiseF(ao.gt, hm_tb_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
544 hm_tp_sf_3 = self.pairwiseF(ao.gt, hm_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
545 tb_tp_sf_3 = self.pairwiseF(ao.gt, tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
546 gt_hm_tb_sf_3 = self.pairwiseF(ao.gt, gt_hm_tb_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
547 gt_hm_tp_sf_3 = self.pairwiseF(ao.gt, gt_hm_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
548 gt_tb_tp_sf_3 = self.pairwiseF(ao.gt, gt_tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
549 hm_tb_tp_sf_3 = self.pairwiseF(ao.gt, hm_tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
550 gt_hm_tb_tp_sf_3 = self.pairwiseF(ao.gt, gt_hm_tb_tp_sf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
551
|
|
mitian@4
|
552 gt_hm_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
553 gt_tb_cnmf_05 = self.pairwiseF(ao.gt, gt_tb_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
554 gt_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
555 hm_tb_cnmf_05 = self.pairwiseF(ao.gt, hm_tb_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
556 hm_tp_cnmf_05 = self.pairwiseF(ao.gt, hm_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
557 tb_tp_cnmf_05 = self.pairwiseF(ao.gt, tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
558 gt_hm_tb_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_tb_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
559 gt_hm_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
560 gt_tb_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
561 hm_tb_tp_cnmf_05 = self.pairwiseF(ao.gt, hm_tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
562 gt_hm_tb_tp_cnmf_05 = self.pairwiseF(ao.gt, gt_hm_tb_tp_cnmf_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
563
|
|
mitian@4
|
564 gt_hm_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
565 gt_tb_cnmf_3 = self.pairwiseF(ao.gt, gt_tb_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
566 gt_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
567 hm_tb_cnmf_3 = self.pairwiseF(ao.gt, hm_tb_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
568 hm_tp_cnmf_3 = self.pairwiseF(ao.gt, hm_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
569 tb_tp_cnmf_3 = self.pairwiseF(ao.gt, tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
570 gt_hm_tb_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_tb_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
571 gt_hm_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
572 gt_tb_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
573 hm_tb_tp_cnmf_3 = self.pairwiseF(ao.gt, hm_tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
574 gt_hm_tb_tp_cnmf_3 = self.pairwiseF(ao.gt, gt_hm_tb_tp_cnmf_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@3
|
575
|
|
mitian@4
|
576 self.writeCombinedRes(outfile7, ao.name, gt_hm_sf_05, gt_hm_sf_3, gt_tb_sf_05, gt_tb_sf_3, gt_tp_sf_05, gt_tp_sf_3, hm_tb_sf_05, hm_tb_sf_3, hm_tp_sf_05, hm_tp_sf_3, tb_tp_sf_05, tb_tp_sf_3,\
|
|
mitian@4
|
577 gt_hm_tb_sf_05, gt_hm_tb_sf_3, gt_hm_tp_sf_05, gt_hm_tp_sf_3, gt_tb_tp_sf_05, gt_tb_tp_sf_3, hm_tb_tp_sf_05, hm_tb_tp_sf_3, gt_hm_tb_tp_sf_05, gt_hm_tb_tp_sf_3)
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mitian@4
|
578
|
|
mitian@4
|
579 self.writeCombinedRes(outfile8, ao.name, gt_hm_cnmf_05, gt_hm_cnmf_3, gt_tb_cnmf_05, gt_tb_cnmf_3, gt_tp_cnmf_05, gt_tp_cnmf_3, hm_tb_cnmf_05, hm_tb_cnmf_3, hm_tp_cnmf_05, hm_tp_cnmf_3, tb_tp_cnmf_05, tb_tp_cnmf_3,\
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|
mitian@4
|
580 gt_hm_tb_cnmf_05, gt_hm_tb_cnmf_3, gt_hm_tp_cnmf_05, gt_hm_tp_cnmf_3, gt_tb_tp_cnmf_05, gt_tb_tp_cnmf_3, hm_tb_tp_cnmf_05, hm_tb_tp_cnmf_3, gt_hm_tb_tp_cnmf_05, gt_hm_tb_tp_cnmf_3)
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mitian@3
|
581
|
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mitian@3
|
582 ############################################################################################################################################
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mitian@3
|
583 # Experiment 3: Pruning boundaries detected by individual boundary algorithms.
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mitian@3
|
584
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mitian@4
|
585 # Use different boundary methods for different features
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mitian@4
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586 gammatone_idxs, harmonic_idxs, timbre_idxs, tempo_idxs = gammatone_sf_idxs, harmonic_sf_idxs, timbre_sf_idxs, tempo_sf_idxs
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mitian@4
|
587 bound_candidates = list(gammatone_idxs) + list(harmonic_idxs) + list(timbre_idxs) + list(tempo_idxs)
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|
mitian@4
|
588 bound_candidates.sort()
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|
mitian@4
|
589
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|
mitian@4
|
590 nBounds = len(bound_candidates)
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mitian@4
|
591 final_idxs = []
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mitian@4
|
592 idx = 0
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mitian@4
|
593 tol = 10 # tolerance window of merging boundary scores
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|
mitian@4
|
594 while idx < nBounds:
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mitian@4
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595 temp = [bound_candidates[idx]]
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mitian@4
|
596 pos = [idx]
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|
mitian@4
|
597 idx += 1
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mitian@4
|
598 while (idx + tol < nBounds and np.max(bound_candidates[idx: idx+tol]) > 0):
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mitian@4
|
599 temp += [bound_candidates[idx+delta] for delta in xrange(tol) if (bound_candidates[idx]+delta in bound_candidates)]
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mitian@4
|
600 pos += [idx+delta for delta in xrange(tol) if (bound_candidates[idx]+delta in bound_candidates)]
|
|
mitian@4
|
601 idx += tol
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|
mitian@4
|
602 if len(temp) == 1:
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|
mitian@4
|
603 final_idxs.append(temp[0])
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|
mitian@4
|
604 else:
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|
mitian@4
|
605 final_idxs.append(int(np.rint(np.mean(temp))))
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|
mitian@4
|
606
|
|
mitian@4
|
607 merged_05 = self.pairwiseF(ao.gt, final_idxs, tolerance=0.5, combine=1.0, idx2time=ao.ssm_timestamps)
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|
mitian@4
|
608 merged_3 = self.pairwiseF(ao.gt, final_idxs, tolerance=3, combine=1.0, idx2time=ao.ssm_timestamps)
|
|
mitian@4
|
609
|
|
mitian@4
|
610 self.writeMergedRes(outfile9, ao.name, merged_05, merged_3)
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|
mitian@4
|
611
|
|
mitian@3
|
612 # if options.BOUNDARY == 'novelty':
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|
mitian@3
|
613 # gammatone_novelty, smoothed_gammatone_novelty, gammatone_bound_idxs = novelty_S.process(ao.gammatone_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
614 # timbre_novelty, smoothed_timbre_novelty, timbre_bound_idxs = novelty_S.process(ao.timbre_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
615 # tempo_novelty, smoothed_harmonic_novelty, tempo_bound_idxs = novelty_S.process(ao.tempo_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
616 # harmonic_novelty, smoothed_tempo_novelty, harmonic_bound_idxs = novelty_S.process(ao.harmonic_ssm, self.kernel_size, peak_picker)
|
|
mitian@3
|
617 #
|
|
mitian@3
|
618 # if options.BOUNDARY == 'cnmf':
|
|
mitian@3
|
619 # gammatone_cnmf_idxs = cnmf_S.segmentation(ao.gammatone_features, rank=rank, R=R, h=8, niter=300)
|
|
mitian@3
|
620 # timbre_cnmf_idxs = cnmf_S.segmentation(ao.timbre_features, rank=rank, R=R, h=h, niter=300)
|
|
mitian@3
|
621 # tempo_cnmf_idxs = cnmf_S.segmentation(ao.tempo_features, rank=rank, R=R, h=h, niter=300)
|
|
mitian@3
|
622 # harmonic_cnmf_idxs = cnmf_S.segmentation(ao.harmonic_features, rank=rank, R=R, h=h, niter=300)
|
|
mitian@3
|
623 #
|
|
mitian@3
|
624 # if options.BOUNDARY == 'foote':
|
|
mitian@3
|
625 # gammatone_foote_idxs = foote_S.segmentation(ao.gammatone_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
626 # timbre_foote_idxs = foote_S.segmentation(ao.timbre_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
627 # tempo_foote_idxs = foote_S.segmentation(ao.tempo_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
628 # harmonic_foote_idxs = foote_S.segmentation(ao.harmonic_features, M=M, Mg=Mg, L=L)
|
|
mitian@3
|
629 #
|
|
mitian@3
|
630 # if options.BOUNDARY == 'sf':
|
|
mitian@3
|
631 # gammatone_sf_idxs = sf_S.segmentation(ao.gammatone_features)
|
|
mitian@3
|
632 # timbre_sf_idxs = sf_S.segmentation(ao.timbre_features)
|
|
mitian@3
|
633 # tempo_sf_idxs = sf_S.segmentation(ao.tempo_features)
|
|
mitian@3
|
634 # harmonic_sf_idxs = sf_S.segmentation(ao.harmonic_features)
|
|
mitian@3
|
635 #
|
|
mitian@3
|
636 # gammatone_novelty_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in gammatone_novelty_peaks]
|
|
mitian@3
|
637 # timbre_novelty_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in timbre_novelty_peaks]
|
|
mitian@3
|
638 # harmonic_novelty_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in harmonic_novelty_peaks]
|
|
mitian@3
|
639 # tempo_novelty_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in tempo_novelty_peaks]
|
|
mitian@3
|
640 #
|
|
mitian@3
|
641 # gammatone_cnmf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in gammatone_cnmf_peaks]
|
|
mitian@3
|
642 # timbre_cnmf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in timbre_cnmf_peaks]
|
|
mitian@3
|
643 # harmonic_cnmf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in harmonic_cnmf_peaks]
|
|
mitian@3
|
644 # tempo_cnmf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in tempo_cnmf_peaks]
|
|
mitian@3
|
645 #
|
|
mitian@3
|
646 # gammatone_sf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in gammatone_sf_peaks]
|
|
mitian@3
|
647 # timbre_sf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in timbre_sf_peaks]
|
|
mitian@3
|
648 # harmonic_sf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in harmonic_sf_peaks]
|
|
mitian@3
|
649 # tempo_sf_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in tempo_sf_peaks]
|
|
mitian@3
|
650 #
|
|
mitian@3
|
651 # gammatone_foote_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in gammatone_foote_peaks]
|
|
mitian@3
|
652 # timbre_foote_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in timbre_foote_peaks]
|
|
mitian@3
|
653 # harmonic_foote_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in harmonic_foote_peaks]
|
|
mitian@3
|
654 # tempo_foote_detection = [0.0] + [ao.ssm_timestamps[int(np.rint(i))] for i in tempo_foote_peaks]
|
|
mitian@3
|
655 #
|
|
mitian@3
|
656 # # Experiment 2: Trying combined features using the best boundary retrieval method
|
|
mitian@3
|
657 # ao_featureset = [ao.gammatone_features, ao.harmonic_features, ao.timbre_features, ao.tempo_features]
|
|
mitian@3
|
658 # feature_sel = [int(x) for x in options.FEATURES if x.isdigit()]
|
|
mitian@3
|
659 # fused_featureset = [ao_featureset[i] for i in feature_sel]
|
|
mitian@3
|
660
|
|
mitian@3
|
661 # if options.LABEL == 'fmc2d':
|
|
mitian@3
|
662 # gammatone_fmc2d_labels = fmc2d_S.compute_similarity(gammatone_bound_idxs, xmeans=True, N=N)
|
|
mitian@3
|
663 # timbre_fmc2d_labels = fmc2d_S.compute_similarity(timbre_bound_idxs, xmeans=True, N=N)
|
|
mitian@3
|
664 # tempo_fmc2d_labels = fmc2d_S.compute_similarity(tempo_bound_idxs, xmeans=True, N=N)
|
|
mitian@3
|
665 # harmonic_fmc2d_labels = fmc2d_S.compute_similarity(harmonic_bound_idxs, xmeans=True, N=N)
|
|
mitian@3
|
666 #
|
|
mitian@3
|
667 # if options.LABEL == 'cnmf':
|
|
mitian@3
|
668 # gammatone_cnmf_labels = cnmf_S.compute_labels(gammatone_bound_idxs, est_bound_idxs, nFrames)
|
|
mitian@3
|
669 # timbre_cnmf_labels = cnmf_S.compute_labels(timbre_bound_idxs, est_bound_idxs, nFrames)
|
|
mitian@3
|
670 # tempo_cnmf_labels = cnmf_S.compute_labels(tempo_bound_idxs, est_bound_idxs, nFrames)
|
|
mitian@3
|
671 # harmonic_cnmf_labels = cnmf_S.compute_labels(harmonic_bound_idxs, est_bound_idxs, nFrames)
|
|
mitian@3
|
672 #
|
|
mitian@3
|
673 #
|
|
mitian@3
|
674
|
|
mi@0
|
675
|
|
mi@0
|
676
|
|
mi@0
|
677 def main():
|
|
mi@0
|
678
|
|
mi@0
|
679 segmenter = SSMseg()
|
|
mi@0
|
680 segmenter.process()
|
|
mi@0
|
681
|
|
mi@0
|
682
|
|
mi@0
|
683 if __name__ == '__main__':
|
|
mi@0
|
684 main()
|
|
mi@0
|
685
|
