def eval2(self,results,tol=0.2): truth = self.gettruth() obeats = [i[0] for i in truth] ebeats = [i[0]*self.params.step for i in results] NP = max(len(obeats), len(ebeats)) N = int(round(max(max(obeats), max(ebeats))*100.)+100) W = int(round(tol*100.*60./median([i[1] for i in truth], len(truth)/2))) ofunc = [0 for i in range(N+W)] efunc = [0 for i in range(N+W)] for i in obeats: ofunc[int(round(i*100.)+W)] = 1 for i in ebeats: efunc[int(round(i*100.)+W)] = 1 assert len(obeats) == sum(ofunc) autocor = 0; m =0 for m in range (-W, W): for i in range(W,N): autocor += ofunc[i] * efunc[i-m] autocor /= float(NP) return autocor
def eval2(self,results,tol=0.2): truth = self.gettruth() obeats = [i[0] for i in truth] ebeats = [i[0]*self.params.step for i in results] NP = max(len(obeats), len(ebeats)) N = int(round(max(max(obeats), max(ebeats))*100.)+100) W = int(round(tol*100.*60./median([i[1] for i in truth], len(truth)/2))) ofunc = [0 for i in range(N+W)] efunc = [0 for i in range(N+W)] for i in obeats: ofunc[int(round(i*100.)+W)] = 1 for i in ebeats: efunc[int(round(i*100.)+W)] = 1 assert len(obeats) == sum(ofunc) autocor = 0; m =0 for m in range (-W, W): for i in range(W,N): autocor += ofunc[i] * efunc[i-m] autocor /= float(NP) return autocor
def evaluation(self, results, tol=0.2, start=5.):
""" beat tracking evaluation function
computes P-score of experimental results (ebeats)
against ground truth annotations (obeats) """
from aubio.median import short_find as median
truth = self.gettruth()
ebeats = [i[0] * self.params.step for i in results]
obeats = [i[0] for i in truth]
# trim anything found before start
while obeats[0] < start:
obeats.pop(0)
while ebeats[0] < start:
ebeats.pop(0)
# maximum number of beats found
NP = max(len(obeats), len(ebeats))
# length of ofunc and efunc vector
N = int(round(max(max(obeats), max(ebeats)) * 100.) + 100)
# compute W median of ground truth tempi
tempi = []
for i in range(1, len(obeats)):
tempi.append(obeats[i] - obeats[i - 1])
W = int(round(tol * 100. * median(tempi, len(tempi) / 2)))
# build ofunc and efunc functions, starting with W zeros
ofunc = [0 for i in range(N + W)]
efunc = [0 for i in range(N + W)]
for i in obeats:
ofunc[int(round(i * 100.) + W)] = 1
for i in ebeats:
efunc[int(round(i * 100.) + W)] = 1
# optional: make sure we didn't miss any beats
assert len(obeats) == sum(ofunc)
assert len(ebeats) == sum(efunc)
# compute auto correlation
autocor = 0
m = 0
for m in range(-W, W):
for i in range(W, N):
autocor += ofunc[i] * efunc[i - m]
autocor /= float(NP)
return autocor
def evaluation(self,results,tol=0.2,start=5.): """ beat tracking evaluation function computes P-score of experimental results (ebeats) against ground truth annotations (obeats) """ from aubio.median import short_find as median truth = self.gettruth() ebeats = [i[0]*self.params.step for i in results] obeats = [i[0] for i in truth] # trim anything found before start while obeats[0] < start: obeats.pop(0) while ebeats[0] < start: ebeats.pop(0) # maximum number of beats found NP = max(len(obeats), len(ebeats)) # length of ofunc and efunc vector N = int(round(max(max(obeats), max(ebeats))*100.)+100) # compute W median of ground truth tempi tempi = [] for i in range(1,len(obeats)): tempi.append(obeats[i]-obeats[i-1]) W = int(round(tol*100.*median(tempi,len(tempi)/2))) # build ofunc and efunc functions, starting with W zeros ofunc = [0 for i in range(N+W)] efunc = [0 for i in range(N+W)] for i in obeats: ofunc[int(round(i*100.)+W)] = 1 for i in ebeats: efunc[int(round(i*100.)+W)] = 1 # optional: make sure we didn't miss any beats assert len(obeats) == sum(ofunc) assert len(ebeats) == sum(efunc) # compute auto correlation autocor = 0; m =0 for m in range (-W, W): for i in range(W,N): autocor += ofunc[i] * efunc[i-m] autocor /= float(NP) return autocor
