export PYTHONPATH=$(BASEDIR)/python
export LD_LIBRARY_PATH=$(BASEDIR)/src/.libs:$(BASEDIR)/ext/.libs
-SOURCE = \
- /archives/samples/DB/PercussivePhrases/CM18/Samba_Audio \
+DETAILSOURCE = \
/var/tmp/Onset-Mirex2005/poly_pitched \
/var/tmp/Onset-Mirex2005/solo_bars_and_bells \
- /var/tmp/Onset-Mirex2005/solo_brass \
/var/tmp/Onset-Mirex2005/solo_drums \
/var/tmp/Onset-Mirex2005/solo_plucked_strings \
/var/tmp/Onset-Mirex2005/solo_singing_voice \
/var/tmp/Onset-Mirex2005/solo_sustained_strings \
/var/tmp/Onset-Mirex2005/solo_winds \
- /var/tmp/Onset-Mirex2005/complex \
- /var/tmp/Onset-Mirex2005
+ /var/tmp/Onset-Mirex2005/complex
-test-aubiocut: $(patsubst %, %.aubiocut, $(SOURCE))
+SOURCE = /var/tmp/Onset-Mirex2005
+
+TESTSOURCE = \
+ /var/tmp/Onset-Mirex2005/solo_bars_and_bells \
+ /var/tmp/Onset-Mirex2005/solo_winds \
+ /archives/samples/DB/PercussivePhrases/CM18/Samba_Audio
+
+test-aubiocut: $(patsubst %, %.aubiocut, $(TESTSOURCE))
+test-aubiodelay: $(patsubst %, %.aubiodelay, $(TESTSOURCE))
+test-aubiowindow: $(patsubst %, %.aubiowindow, $(TESTSOURCE))
+
+final-aubiocut: $(patsubst %, %.aubiocut, $(DETAILSOURCE) $(SOURCE))
+final-aubiodelay: $(patsubst %, %.aubiodelay, $(SOURCE))
+final-aubiowindow: $(patsubst %, %.aubiowindow, $(SOURCE))
%.aubiocut: %
rm -f `basename $<`.aubiocut
./bench-onset $< | tee `basename $<`.aubiocut
- diff `basename $<`.aubiocut.ref `basename $<`.aubiocut
+ -diff `basename $<`.aubiocut.ref `basename $<`.aubiocut
+
+%.aubiodelay: %
+ rm -f `basename $@`
+ ./bench-delay $< | tee `basename $@`
+ -diff `basename $@`.ref `basename $@`
+
+%.aubiowindow: %
+ rm -f `basename $@`
+ ./bench-window $< | tee `basename $@`
+ -diff `basename $@`.ref `basename $@`
--- /dev/null
+#! /usr/bin/python
+
+from aubio.bench.node import *
+from aubio.tasks import *
+
+from benchonset import mmean, stdev, benchonset
+
+class mybenchonset(benchonset):
+
+ def run_bench(self,modes=['dual'],thresholds=[0.5]):
+ from os.path import dirname,basename
+ self.modes = modes
+ self.thresholds = thresholds
+ self.pretty_titles()
+ for mode in self.modes:
+ d = []
+ outplot = "_-_".join(("delay",mode,
+ basename(self.datadir) ))
+
+ self.params.onsetmode = mode
+ self.params.threshold = thresholds[0]
+
+ self.params.localmin = False
+ self.params.delay = 0.
+
+ self.dir_exec()
+ self.dir_eval()
+ self.pretty_print()
+ self.plotdiffs(d,plottitle="Causal")
+
+ self.params.localmin = True
+ self.params.delay = 0.
+ self.dir_exec()
+ self.dir_eval()
+ self.pretty_print()
+ self.plotdiffs(d,plottitle="Local min")
+
+ self.params.localmin = False
+ self.params.delay = 6.
+ self.dir_exec()
+ self.dir_eval()
+ self.pretty_print()
+ self.plotdiffs(d,plottitle="Fixed delay")
+
+ self.plotplotdiffs(d)
+ self.plotplotdiffs(d,outplot=outplot,extension="png")
+ self.plotplotdiffs(d,outplot=outplot,extension="ps")
+ self.plotplotdiffs(d,outplot=outplot,extension="svg")
+
+
+if __name__ == "__main__":
+ import sys
+ if len(sys.argv) > 1: datapath = sys.argv[1]
+ else: print "ERR: a path is required"; sys.exit(1)
+ modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
+ #modes = [ 'complex' ]
+ thresholds = [ 0.5]
+ #thresholds = [1.5]
+
+ #datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
+ respath = '/var/tmp/DB-testings'
+
+ benchonset = mybenchonset(datapath,respath,checkres=True,checkanno=True)
+ benchonset.params = taskparams()
+ benchonset.task = taskonset
+ benchonset.valuesdict = {}
+
+ try:
+ #benchonset.auto_learn2(modes=modes)
+ benchonset.run_bench(modes=modes,thresholds=thresholds)
+ except KeyboardInterrupt:
+ sys.exit(1)
#! /usr/bin/python
-from aubio.bench.node import *
from aubio.tasks import *
+from benchonset import mmean, stdev, benchonset
-
-
-def mmean(l):
- return sum(l)/float(len(l))
-
-def stdev(l):
- smean = 0
- lmean = mmean(l)
- for i in l:
- smean += (i-lmean)**2
- smean *= 1. / len(l)
- return smean**.5
-
-class benchonset(bench):
-
- """ list of values to store per file """
- valuenames = ['orig','missed','Tm','expc','bad','Td']
- """ list of lists to store per file """
- valuelists = ['l','labs']
- """ list of values to print per dir """
- printnames = [ 'mode', 'thres', 'dist', 'prec', 'recl',
- 'Ttrue', 'Tfp', 'Tfn', 'Tm', 'Td',
- 'aTtrue', 'aTfp', 'aTfn', 'aTm', 'aTd',
- 'mean', 'smean', 'amean', 'samean']
-
- """ per dir """
- formats = {'mode': "%12s" , 'thres': "%5.4s",
- 'dist': "%5.4s", 'prec': "%5.4s", 'recl': "%5.4s",
- 'Ttrue': "%5.4s", 'Tfp': "%5.4s", 'Tfn': "%5.4s",
- 'Tm': "%5.4s", 'Td': "%5.4s",
- 'aTtrue':"%5.4s", 'aTfp': "%5.4s", 'aTfn': "%5.4s",
- 'aTm': "%5.4s", 'aTd': "%5.4s",
- 'mean': "%5.40s", 'smean': "%5.40s",
- 'amean': "%5.40s", 'samean': "%5.40s"}
-
- def dir_eval(self):
- """ evaluate statistical data over the directory """
- totaltrue = sum(self.v['expc'])-sum(self.v['bad'])-sum(self.v['Td'])
- totalfp = sum(self.v['bad'])+sum(self.v['Td'])
- totalfn = sum(self.v['missed'])+sum(self.v['Tm'])
- self.P = 100*float(totaltrue)/max(totaltrue + totalfp,1)
- self.R = 100*float(totaltrue)/max(totaltrue + totalfn,1)
- if self.R < 0: self.R = 0
- self.F = 2.* self.P*self.R / max(float(self.P+self.R),1)
- N = float(len(self.reslist))
- self.v['mode'] = self.params.onsetmode
- self.v['thres'] = self.params.threshold
- self.v['thres'] = "%2.3f" % self.params.threshold
- self.v['dist'] = "%2.3f" % self.F
- self.v['prec'] = "%2.3f" % self.P
- self.v['recl'] = "%2.3f" % self.R
- self.v['Ttrue'] = totaltrue
- self.v['Tfp'] = totalfp
- self.v['Tfn'] = totalfn
- self.v['aTtrue'] = totaltrue/N
- self.v['aTfp'] = totalfp/N
- self.v['aTfn'] = totalfn/N
- self.v['aTm'] = sum(self.v['Tm'])/N
- self.v['aTd'] = sum(self.v['Td'])/N
- self.v['Tm'] = sum(self.v['Tm'])
- self.v['Td'] = sum(self.v['Td'])
- self.v['mean'] = mmean(self.v['l'])
- self.v['smean'] = stdev(self.v['l'])
- self.v['amean'] = mmean(self.v['labs'])
- self.v['samean'] = stdev(self.v['labs'])
+class mybenchonset(benchonset):
def run_bench(self,modes=['dual'],thresholds=[0.5]):
- self.modes = modes
+ from os.path import dirname,basename
self.thresholds = thresholds
self.pretty_titles()
- for mode in self.modes:
+ d,e,f = [],[],[]
+ for mode in modes:
+ self.vlist = []
self.params.onsetmode = mode
for threshold in self.thresholds:
self.params.threshold = threshold
self.dir_eval()
self.pretty_print()
#print self.v
+ self.vlist.append(self.v)
+ self.plotroc(d)
+ self.plotfmeas(e)
+ self.plotpr(f)
+ #print vlist
+ #self.plotplotroc(d)
+ #self.plotplotfmeas(e)
+ #self.plotplotpr(f)
+ outplot = basename(self.datadir)
+ for ext in ("png","svg","ps"):
+ self.plotplotroc(d,outplot=outplot,extension=ext)
+ self.plotplotfmeas(e,outplot=outplot,extension=ext)
+ self.plotplotpr(f,outplot=outplot,extension=ext)
+
def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]):
""" simple dichotomia like algorithm to optimise threshold """
lessF = self.F
for i in range(steps):
+ self.params.localmin = True
+ self.params.delay = 1.
+ self.dir_exec()
+ self.dir_eval()
self.params.threshold = ( lesst + topt ) * .5
self.dir_exec()
self.dir_eval()
import sys
if len(sys.argv) > 1: datapath = sys.argv[1]
else: print "ERR: a path is required"; sys.exit(1)
- modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
- #modes = [ 'mkl' ]
+ modes = ['complex', 'energy', 'phase', 'hfc', 'specdiff', 'kl', 'mkl', 'dual']
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]
- #thresholds = [1.5]
+ #modes = [ 'hfc' ]
+ #thresholds = [0.1, 1.5]
#datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
respath = '/var/tmp/DB-testings'
- benchonset = benchonset(datapath,respath,checkres=True,checkanno=True)
+ benchonset = mybenchonset(datapath,respath,checkres=True,checkanno=True)
benchonset.params = taskparams()
benchonset.task = taskonset
benchonset.valuesdict = {}
--- /dev/null
+#! /usr/bin/python
+
+from aubio.tasks import *
+
+from benchonset import mmean, stdev, plotdiffs, plotplotdiffs, benchonset
+
+class mybenchonset(benchonset):
+
+ def run_bench(self,modes=['dual'],thresholds=[0.5]):
+ from os.path import dirname,basename
+ self.thresholds = thresholds
+ self.pretty_titles()
+ for mode in modes:
+
+ self.params.onsetmode = mode
+ self.params.threshold = thresholds[0]
+ self.params.localmin = False
+
+ for delay in (0., 4.):
+ d = []
+ outplot = "_-_".join(("window",mode,"delay-%s" % delay,
+ basename(self.datadir) ))
+ self.params.delay = delay
+
+ for buf in (2048, 1024, 512):
+ for hop in (buf/2, buf/4):
+ self.params.bufsize = buf
+ self.params.hopsize = hop
+ self.params.step = float(self.params.hopsize)/float(self.params.samplerate)
+
+ self.dir_exec()
+ self.dir_eval()
+ self.pretty_print()
+ plotdiffs(self.v,d,plottitle="%s %s" % (buf,hop))
+
+ plotplotdiffs(d)
+ plotplotdiffs(d,outplot=outplot,extension="png")
+ plotplotdiffs(d,outplot=outplot,extension="ps")
+ plotplotdiffs(d,outplot=outplot,extension="svg")
+
+
+if __name__ == "__main__":
+ import sys
+ if len(sys.argv) > 1: datapath = sys.argv[1]
+ else: print "ERR: a path is required"; sys.exit(1)
+ modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual']
+ #modes = [ 'complex' ]
+ thresholds = [ 0.5]
+ #thresholds = [1.5]
+
+ #datapath = "%s%s" % (DATADIR,'/onset/DB/*/')
+ respath = '/var/tmp/DB-testings'
+
+ benchonset = mybenchonset(datapath,respath,checkres=True,checkanno=True)
+ benchonset.params = taskparams()
+ benchonset.task = taskonset
+ benchonset.valuesdict = {}
+
+ try:
+ #benchonset.auto_learn2(modes=modes)
+ benchonset.run_bench(modes=modes,thresholds=thresholds)
+ except KeyboardInterrupt:
+ sys.exit(1)
--- /dev/null
+#! /usr/bin/python
+
+from aubio.bench.node import *
+from os.path import dirname,basename
+
+def mmean(l):
+ return sum(l)/max(float(len(l)),1)
+
+def stdev(l):
+ smean = 0
+ if not len(l): return smean
+ lmean = mmean(l)
+ for i in l:
+ smean += (i-lmean)**2
+ smean *= 1. / len(l)
+ return smean**.5
+
+class benchonset(bench):
+
+ """ list of values to store per file """
+ valuenames = ['orig','missed','Tm','expc','bad','Td']
+ """ list of lists to store per file """
+ valuelists = ['l','labs']
+ """ list of values to print per dir """
+ printnames = [ 'mode', 'thres', 'dist', 'prec', 'recl',
+ 'GD', 'FP',
+ 'Torig', 'Ttrue', 'Tfp', 'Tfn', 'TTm', 'TTd',
+ 'aTtrue', 'aTfp', 'aTfn', 'aTm', 'aTd',
+ 'mean', 'smean', 'amean', 'samean']
+
+ """ per dir """
+ formats = {'mode': "%12s" , 'thres': "%5.4s",
+ 'dist': "%5.4s", 'prec': "%5.4s", 'recl': "%5.4s",
+ 'Torig': "%5.4s", 'Ttrue': "%5.4s", 'Tfp': "%5.4s", 'Tfn': "%5.4s",
+ 'TTm': "%5.4s", 'TTd': "%5.4s",
+ 'aTtrue':"%5.4s", 'aTfp': "%5.4s", 'aTfn': "%5.4s",
+ 'aTm': "%5.4s", 'aTd': "%5.4s",
+ 'mean': "%5.40s", 'smean': "%5.40s",
+ 'amean': "%5.40s", 'samean': "%5.40s",
+ "GD": "%5.4s", "FP": "%5.4s",
+ "GDm": "%5.4s", "FPd": "%5.4s"}
+
+ def dir_eval(self):
+ """ evaluate statistical data over the directory """
+ v = self.v
+
+ v['mode'] = self.params.onsetmode
+ v['thres'] = self.params.threshold
+
+ v['Torig'] = sum(v['orig'])
+ v['TTm'] = sum(v['Tm'])
+ v['TTd'] = sum(v['Td'])
+ v['Texpc'] = sum(v['expc'])
+ v['Tbad'] = sum(v['bad'])
+ v['Tmissed'] = sum(v['missed'])
+ v['aTm'] = mmean(v['Tm'])
+ v['aTd'] = mmean(v['Td'])
+
+ v['mean'] = mmean(v['l'])
+ v['smean'] = stdev(v['l'])
+
+ v['amean'] = mmean(v['labs'])
+ v['samean'] = stdev(v['labs'])
+
+ # old type calculations
+ # good detection rate
+ v['GD'] = 100.*(v['Torig']-v['Tmissed']-v['TTm'])/v['Torig']
+ # false positive rate
+ v['FP'] = 100.*(v['Tbad']+v['TTd'])/v['Torig']
+ # good detection counting merged detections as good
+ v['GDm'] = 100.*(v['Torig']-v['Tmissed'])/v['Torig']
+ # false positives counting doubled as good
+ v['FPd'] = 100.*v['Tbad']/v['Torig']
+
+ # mirex type annotations
+ totaltrue = v['Texpc']-v['Tbad']-v['TTd']
+ totalfp = v['Tbad']+v['TTd']
+ totalfn = v['Tmissed']+v['TTm']
+ self.v['Ttrue'] = totaltrue
+ self.v['Tfp'] = totalfp
+ self.v['Tfn'] = totalfn
+ # average over the number of annotation files
+ N = float(len(self.reslist))
+ self.v['aTtrue'] = totaltrue/N
+ self.v['aTfp'] = totalfp/N
+ self.v['aTfn'] = totalfn/N
+
+ # F-measure
+ self.P = 100.*float(totaltrue)/max(totaltrue + totalfp,1)
+ self.R = 100.*float(totaltrue)/max(totaltrue + totalfn,1)
+ #if self.R < 0: self.R = 0
+ self.F = 2.* self.P*self.R / max(float(self.P+self.R),1)
+ self.v['dist'] = self.F
+ self.v['prec'] = self.P
+ self.v['recl'] = self.R
+
+ def plotroc(self,d,plottitle=""):
+ import Gnuplot, Gnuplot.funcutils
+ gd = []
+ fp = []
+ for i in self.vlist:
+ gd.append(i['GD'])
+ fp.append(i['FP'])
+ d.append(Gnuplot.Data(fp, gd, with='linespoints',
+ title="%s %s" % (plottitle,i['mode']) ))
+
+ def plotplotroc(self,d,outplot=0,extension='ps'):
+ import Gnuplot, Gnuplot.funcutils
+ from sys import exit
+ g = Gnuplot.Gnuplot(debug=0, persist=1)
+ if outplot:
+ if extension == 'ps': ext, extension = '.ps' , 'postscript'
+ elif extension == 'png': ext, extension = '.png', 'png'
+ elif extension == 'svg': ext, extension = '.svg', 'svg'
+ else: exit("ERR: unknown plot extension")
+ g('set terminal %s' % extension)
+ g('set output \'roc-%s%s\'' % (outplot,ext))
+ xmax = 30 #max(fp)
+ ymin = 50
+ g('set xrange [0:%f]' % xmax)
+ g('set yrange [%f:100]' % ymin)
+ # grid set
+ g('set grid')
+ g('set xtics 0,5,%f' % xmax)
+ g('set ytics %f,5,100' % ymin)
+ g('set key 27,65')
+ #g('set format \"%g\"')
+ g.title(basename(self.datadir))
+ g.xlabel('false positives (%)')
+ g.ylabel('correct detections (%)')
+ g.plot(*d)
+
+ def plotpr(self,d,plottitle=""):
+ import Gnuplot, Gnuplot.funcutils
+ x = []
+ y = []
+ for i in self.vlist:
+ x.append(i['prec'])
+ y.append(i['recl'])
+ d.append(Gnuplot.Data(x, y, with='linespoints',
+ title="%s %s" % (plottitle,i['mode']) ))
+
+ def plotplotpr(self,d,outplot=0,extension='ps'):
+ import Gnuplot, Gnuplot.funcutils
+ from sys import exit
+ g = Gnuplot.Gnuplot(debug=0, persist=1)
+ if outplot:
+ if extension == 'ps': ext, extension = '.ps' , 'postscript'
+ elif extension == 'png': ext, extension = '.png', 'png'
+ elif extension == 'svg': ext, extension = '.svg', 'svg'
+ else: exit("ERR: unknown plot extension")
+ g('set terminal %s' % extension)
+ g('set output \'pr-%s%s\'' % (outplot,ext))
+ g.title(basename(self.datadir))
+ g.xlabel('Recall (%)')
+ g.ylabel('Precision (%)')
+ g.plot(*d)
+
+ def plotfmeas(self,d,plottitle=""):
+ import Gnuplot, Gnuplot.funcutils
+ x,y = [],[]
+ for i in self.vlist:
+ x.append(i['thres'])
+ y.append(i['dist'])
+ d.append(Gnuplot.Data(x, y, with='linespoints',
+ title="%s %s" % (plottitle,i['mode']) ))
+
+ def plotplotfmeas(self,d,outplot="",extension='ps', title="F-measure"):
+ import Gnuplot, Gnuplot.funcutils
+ from sys import exit
+ g = Gnuplot.Gnuplot(debug=0, persist=1)
+ if outplot:
+ if extension == 'ps': terminal = 'postscript'
+ elif extension == 'png': terminal = 'png'
+ elif extension == 'svg': terminal = 'svg'
+ else: exit("ERR: unknown plot extension")
+ g('set terminal %s' % terminal)
+ g('set output \'fmeas-%s.%s\'' % (outplot,extension))
+ g.xlabel('threshold \\delta')
+ g.ylabel('F-measure (%)')
+ g('set xrange [0:1.2]')
+ g('set yrange [0:100]')
+ g.title(basename(self.datadir))
+ # grid set
+ #g('set grid')
+ #g('set xtics 0,5,%f' % xmax)
+ #g('set ytics %f,5,100' % ymin)
+ #g('set key 27,65')
+ #g('set format \"%g\"')
+ g.plot(*d)
+
+ def plotdiffs(self,d,plottitle=""):
+ import Gnuplot, Gnuplot.funcutils
+ v = self.v
+ l = v['l']
+ mean = v['mean']
+ smean = v['smean']
+ amean = v['amean']
+ samean = v['samean']
+ val = []
+ per = [0] * 100
+ for i in range(0,100):
+ val.append(i*.001-.05)
+ for j in l:
+ if abs(j-val[i]) <= 0.001:
+ per[i] += 1
+ total = v['Torig']
+ for i in range(len(per)): per[i] /= total/100.
+
+ d.append(Gnuplot.Data(val, per, with='fsteps',
+ title="%s %s" % (plottitle,v['mode']) ))
+ #d.append('mean=%f,sigma=%f,eps(x) title \"\"'% (mean,smean))
+ #d.append('mean=%f,sigma=%f,eps(x) title \"\"'% (amean,samean))
+
+
+ def plotplotdiffs(self,d,outplot=0,extension='ps'):
+ import Gnuplot, Gnuplot.funcutils
+ from sys import exit
+ g = Gnuplot.Gnuplot(debug=0, persist=1)
+ if outplot:
+ if extension == 'ps': ext, extension = '.ps' , 'postscript'
+ elif extension == 'png': ext, extension = '.png', 'png'
+ elif extension == 'svg': ext, extension = '.svg', 'svg'
+ else: exit("ERR: unknown plot extension")
+ g('set terminal %s' % extension)
+ g('set output \'diffhist-%s%s\'' % (outplot,ext))
+ g('eps(x) = 1./(sigma*(2.*3.14159)**.5) * exp ( - ( x - mean ) ** 2. / ( 2. * sigma ** 2. ))')
+ g.title(basename(self.datadir))
+ g.xlabel('delay to hand-labelled onset (s)')
+ g.ylabel('% number of correct detections / ms ')
+ g('set xrange [-0.05:0.05]')
+ g('set yrange [0:50]')
+ g.plot(*d)
+
+
projects / aubio.git / commitdiff