add beat p-score evaluation

add beat p-score evaluation

diff --git a/python/aubio/task/beat.py b/python/aubio/task/beat.py
index 7edb1f8..f79cc8e 100644 (file)
--- a/python/aubio/task/beat.py
+++ b/python/aubio/task/beat.py
@@ -40,7 +40,7 @@ class taskbeat(taskonset):
                                period = (60 * self.params.samplerate) / ((now - self.old) * self.params.hopsize)
                                self.old = now
                                return now*self.btstep*self.params.step,period
-       
+
        def eval(self,results,tol=0.20,tolcontext=0.25):
                obeats = self.gettruth()
                etime = [result[0] for result in results]
@@ -169,7 +169,64 @@ class taskbeat(taskonset):
 #                      while freq[i]>freqs[j]:
 #                              j += 1
                        
+       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 gettruth(self):
                import os.path
                from aubio.txtfile import read_datafile