- Timestamp:
- Feb 23, 2006, 3:23:30 AM (19 years ago)
- Branches:
- feature/autosink, feature/cnn, feature/cnn_org, feature/constantq, feature/crepe, feature/crepe_org, feature/pitchshift, feature/pydocstrings, feature/timestretch, fix/ffmpeg5, master, pitchshift, sampler, timestretch, yinfft+
- Children:
- ee7b9da
- Parents:
- a12563ea
- Location:
- python/test/bench/onset
- Files:
-
- 4 edited
-
bench-delay (modified) (3 diffs)
-
bench-onset (modified) (3 diffs)
-
bench-window (modified) (4 diffs)
-
benchonset.py (modified) (4 diffs)
Legend:
- Unmodified
- Added
- Removed
-
python/test/bench/onset/bench-delay
ra12563ea r6ae3a77 1 1 #! /usr/bin/python 2 2 3 from aubio.bench.node import *4 3 from aubio.tasks import * 5 4 … … 10 9 def run_bench(self,modes=['dual'],thresholds=[0.5]): 11 10 from os.path import dirname,basename 12 self.modes = modes13 11 self.thresholds = thresholds 14 12 self.pretty_titles() 15 for mode in self.modes: 13 14 for mode in modes: 16 15 d = [] 17 outplot = "_-_".join(("delay",mode,18 basename(self.datadir) ))19 20 16 self.params.onsetmode = mode 17 self.params.localmin = True 18 self.params.delay = 1. 21 19 self.params.threshold = thresholds[0] 22 20 # 23 21 self.params.localmin = False 24 22 self.params.delay = 0. 25 26 self.dir_exec() 27 self.dir_eval() 28 self.pretty_print() 23 self.dir_eval_print() 29 24 self.plotdiffs(d,plottitle="Causal") 30 25 # 31 26 self.params.localmin = True 32 27 self.params.delay = 0. 33 self.dir_exec() 34 self.dir_eval() 35 self.pretty_print() 28 self.dir_eval_print() 36 29 self.plotdiffs(d,plottitle="Local min") 37 30 38 31 self.params.localmin = False 39 32 self.params.delay = 6. 40 self.dir_exec() 41 self.dir_eval() 42 self.pretty_print() 33 self.dir_eval_print() 43 34 self.plotdiffs(d,plottitle="Fixed delay") 44 35 45 self.plotplotdiffs(d) 46 self.plotplotdiffs(d,outplot=outplot,extension="png") 47 self.plotplotdiffs(d,outplot=outplot,extension="ps") 48 self.plotplotdiffs(d,outplot=outplot,extension="svg") 49 36 #self.plotplotdiffs(d) 37 outplot = "_-_".join(("delay",mode,basename(self.datadir) )) 38 for ext in ("png","svg","ps"): 39 self.plotplotdiffs(d,outplot=outplot,extension=ext) 50 40 51 41 if __name__ == "__main__": … … 53 43 if len(sys.argv) > 1: datapath = sys.argv[1] 54 44 else: print "ERR: a path is required"; sys.exit(1) 55 modes = ['complex', 'energy', 'phase', ' specdiff', 'kl', 'mkl', 'dual']56 # modes = [ 'complex']57 thresholds = [ 0.5]58 #thresholds = [1.5]45 modes = ['complex', 'energy', 'phase', 'hfc', 'specdiff', 'kl', 'mkl', 'dual'] 46 #thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] 47 #modes = [ 'hfc' ] 48 thresholds = [0.5] 59 49 60 50 #datapath = "%s%s" % (DATADIR,'/onset/DB/*/') -
python/test/bench/onset/bench-onset
ra12563ea r6ae3a77 11 11 self.thresholds = thresholds 12 12 self.pretty_titles() 13 d,e,f =[],[],[]13 d,e,f,g = [],[],[],[] 14 14 for mode in modes: 15 15 self.vlist = [] 16 16 self.params.onsetmode = mode 17 #self.params.localmin = True 18 self.params.delay = 2. 19 self.params.bufsize = 1024 20 self.params.hopsize = 256 21 self.params.step = float(self.params.hopsize)/float(self.params.samplerate) 22 #self.params.mintol = 8. 23 17 24 for threshold in self.thresholds: 18 25 self.params.threshold = threshold 19 self.dir_exec() 20 self.dir_eval() 21 self.pretty_print() 22 #print self.v 26 self.dir_eval_print() 23 27 self.vlist.append(self.v) 24 28 self.plotroc(d) 25 29 self.plotfmeas(e) 26 30 self.plotpr(f) 27 #print vlist 31 #self.plothistcat(g) 32 33 34 28 35 #self.plotplotroc(d) 29 36 #self.plotplotfmeas(e) … … 34 41 self.plotplotfmeas(e,outplot=outplot,extension=ext) 35 42 self.plotplotpr(f,outplot=outplot,extension=ext) 36 37 38 def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]): 39 """ simple dichotomia like algorithm to optimise threshold """ 40 self.modes = modes 41 self.pretty_titles() 42 for mode in self.modes: 43 steps = 11 44 lesst = thresholds[0] 45 topt = thresholds[1] 46 self.params.onsetmode = mode 47 48 self.params.threshold = topt 49 self.dir_exec() 50 self.dir_eval() 51 self.pretty_print() 52 topF = self.F 53 54 self.params.threshold = lesst 55 self.dir_exec() 56 self.dir_eval() 57 self.pretty_print() 58 lessF = self.F 59 60 for i in range(steps): 61 self.params.localmin = True 62 self.params.delay = 1. 63 self.dir_exec() 64 self.dir_eval() 65 self.params.threshold = ( lesst + topt ) * .5 66 self.dir_exec() 67 self.dir_eval() 68 self.pretty_print() 69 if self.F == 100.0 or self.F == topF: 70 print "assuming we converged, stopping" 71 break 72 #elif abs(self.F - topF) < 0.01 : 73 # print "done converging" 74 # break 75 if topF < self.F: 76 #lessF = topF 77 #lesst = topt 78 topF = self.F 79 topt = self.params.threshold 80 elif lessF < self.F: 81 lessF = self.F 82 lesst = self.params.threshold 83 if topt == lesst: 84 lesst /= 2. 85 86 def auto_learn2(self,modes=['dual'],thresholds=[0.00001,1.0]): 87 """ simple dichotomia like algorithm to optimise threshold """ 88 self.modes = modes 89 self.pretty_titles([]) 90 for mode in self.modes: 91 steps = 10 92 step = 0.4 93 self.params.onsetmode = mode 94 self.params.threshold = thresholds[0] 95 cur = 0 96 97 for i in range(steps): 98 self.dir_exec() 99 self.dir_eval() 100 self.pretty_print() 101 new = self.P 102 if self.R == 0.0: 103 #print "Found maximum, highering" 104 step /= 2. 105 self.params.threshold -= step 106 elif new == 100.0: 107 #print "Found maximum, highering" 108 step *= .99 109 self.params.threshold += step 110 elif cur > new: 111 #print "lower" 112 step /= 2. 113 self.params.threshold -= step 114 elif cur < new: 115 #print "higher" 116 step *= .99 117 self.params.threshold += step 118 else: 119 print "Assuming we converged" 120 break 121 cur = new 122 43 #self.plotplothistcat(g,outplot=outplot,extension=ext) 123 44 124 45 if __name__ == "__main__": … … 129 50 thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] 130 51 #modes = [ 'hfc' ] 131 #thresholds = [0. 1, 1.5]52 #thresholds = [0.5] 132 53 133 54 #datapath = "%s%s" % (DATADIR,'/onset/DB/*/') -
python/test/bench/onset/bench-window
ra12563ea r6ae3a77 3 3 from aubio.tasks import * 4 4 5 from benchonset import mmean, stdev, plotdiffs, plotplotdiffs,benchonset5 from benchonset import mmean, stdev, benchonset 6 6 7 7 class mybenchonset(benchonset): … … 11 11 self.thresholds = thresholds 12 12 self.pretty_titles() 13 13 14 for mode in modes: 14 15 15 16 self.params.onsetmode = mode 17 self.params.localmin = True 18 self.params.delay = 1. 16 19 self.params.threshold = thresholds[0] 17 20 self.params.localmin = False 18 21 # 19 22 for delay in (0., 4.): 20 23 d = [] 21 outplot = "_-_".join(("window",mode,"delay-%s" % delay,22 basename(self.datadir) ))23 24 self.params.delay = delay 24 25 25 for buf in (2048, 1024, 512): 26 26 for hop in (buf/2, buf/4): … … 28 28 self.params.hopsize = hop 29 29 self.params.step = float(self.params.hopsize)/float(self.params.samplerate) 30 31 self.dir_exec() 32 self.dir_eval() 33 self.pretty_print() 34 plotdiffs(self.v,d,plottitle="%s %s" % (buf,hop)) 35 36 plotplotdiffs(d) 37 plotplotdiffs(d,outplot=outplot,extension="png") 38 plotplotdiffs(d,outplot=outplot,extension="ps") 39 plotplotdiffs(d,outplot=outplot,extension="svg") 40 30 self.dir_eval_print() 31 self.plotdiffs(d,plottitle="%s %s" % (buf,hop)) 32 #plotplotdiffs(d) 33 outplot = "_-_".join(("window",mode,"delay-%s" % int(delay), 34 basename(self.datadir) )) 35 for ext in ("png","svg","ps"): 36 self.plotplotdiffs(d,outplot=outplot,extension=ext) 41 37 42 38 if __name__ == "__main__": … … 44 40 if len(sys.argv) > 1: datapath = sys.argv[1] 45 41 else: print "ERR: a path is required"; sys.exit(1) 46 modes = ['complex', 'energy', 'phase', ' specdiff', 'kl', 'mkl', 'dual']47 # modes = [ 'complex']48 thresholds = [ 0.5]49 #thresholds = [1.5]42 modes = ['complex', 'energy', 'phase', 'hfc', 'specdiff', 'kl', 'mkl', 'dual'] 43 #thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] 44 #modes = [ 'hfc' ] 45 thresholds = [0.5] 50 46 51 47 #datapath = "%s%s" % (DATADIR,'/onset/DB/*/') -
python/test/bench/onset/benchonset.py
ra12563ea r6ae3a77 47 47 v['mode'] = self.params.onsetmode 48 48 v['thres'] = self.params.threshold 49 v['bufsize'] = self.params.bufsize 50 v['hopsize'] = self.params.hopsize 51 v['silence'] = self.params.silence 52 v['mintol'] = self.params.mintol 49 53 50 54 v['Torig'] = sum(v['orig']) … … 95 99 self.v['recl'] = self.R 96 100 101 102 """ 103 Plot functions 104 """ 105 97 106 def plotroc(self,d,plottitle=""): 98 107 import Gnuplot, Gnuplot.funcutils … … 188 197 #g('set key 27,65') 189 198 #g('set format \"%g\"') 199 g.plot(*d) 200 201 def plotfmeasvar(self,d,var,plottitle=""): 202 import Gnuplot, Gnuplot.funcutils 203 x,y = [],[] 204 for i in self.vlist: 205 x.append(i[var]) 206 y.append(i['dist']) 207 d.append(Gnuplot.Data(x, y, with='linespoints', 208 title="%s %s" % (plottitle,i['mode']) )) 209 210 def plotplotfmeasvar(self,d,var,outplot="",extension='ps', title="F-measure"): 211 import Gnuplot, Gnuplot.funcutils 212 from sys import exit 213 g = Gnuplot.Gnuplot(debug=0, persist=1) 214 if outplot: 215 if extension == 'ps': terminal = 'postscript' 216 elif extension == 'png': terminal = 'png' 217 elif extension == 'svg': terminal = 'svg' 218 else: exit("ERR: unknown plot extension") 219 g('set terminal %s' % terminal) 220 g('set output \'fmeas-%s.%s\'' % (outplot,extension)) 221 g.xlabel(var) 222 g.ylabel('F-measure (%)') 223 #g('set xrange [0:1.2]') 224 g('set yrange [0:100]') 225 g.title(basename(self.datadir)) 190 226 g.plot(*d) 191 227 … … 230 266 g.ylabel('% number of correct detections / ms ') 231 267 g('set xrange [-0.05:0.05]') 232 g('set yrange [0:50]') 233 g.plot(*d) 234 235 268 g('set yrange [0:20]') 269 g.plot(*d) 270 271 272 def plothistcat(self,d,plottitle=""): 273 import Gnuplot, Gnuplot.funcutils 274 total = v['Torig'] 275 for i in range(len(per)): per[i] /= total/100. 276 277 d.append(Gnuplot.Data(val, per, with='fsteps', 278 title="%s %s" % (plottitle,v['mode']) )) 279 #d.append('mean=%f,sigma=%f,eps(x) title \"\"'% (mean,smean)) 280 #d.append('mean=%f,sigma=%f,eps(x) title \"\"'% (amean,samean)) 281 282 283 def plotplothistcat(self,d,outplot=0,extension='ps'): 284 import Gnuplot, Gnuplot.funcutils 285 from sys import exit 286 g = Gnuplot.Gnuplot(debug=0, persist=1) 287 if outplot: 288 if extension == 'ps': ext, extension = '.ps' , 'postscript' 289 elif extension == 'png': ext, extension = '.png', 'png' 290 elif extension == 'svg': ext, extension = '.svg', 'svg' 291 else: exit("ERR: unknown plot extension") 292 g('set terminal %s' % extension) 293 g('set output \'diffhist-%s%s\'' % (outplot,ext)) 294 g('eps(x) = 1./(sigma*(2.*3.14159)**.5) * exp ( - ( x - mean ) ** 2. / ( 2. * sigma ** 2. ))') 295 g.title(basename(self.datadir)) 296 g.xlabel('delay to hand-labelled onset (s)') 297 g.ylabel('% number of correct detections / ms ') 298 g('set xrange [-0.05:0.05]') 299 g('set yrange [0:20]') 300 g.plot(*d) 301 302
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