comparison Syncopation models/SG.py @ 0:76ce27beba95

Have uploaded the syncopation dataset and audio wavefies.

+'''
+Author: Chunyang Song
+Institution: Centre for Digital Music, Queen Mary University of London
+
+** Sioros and Guedes's Model **
+
+Algorithm:
+
+Only applicable to monorhythms.
+
+This version of implementation follows the description in authors' 2011 ISMIR paper and assumes each bar of rhythm is looped.
+Therefore each bar is calculated seperatedly in a loop-mode and summed up in the end as the total syncopation. 
+
+'''
+
+from MeterStructure import MeterStructure
+from math import pow
+
+def subdivide(sequence, segments_num):
+	subSeq = []
+	if len(sequence) % segments_num != 0:
+		print 'Error: rhythm segment cannot be equally subdivided '
+	else:
+		n = len(sequence) / segments_num
+		start , end = 0, n
+		for i in range(segments_num):
+			subSeq.append(sequence[start : end])
+			start = end
+			end = end + n
+	
+	return subSeq
+
+
+def sgModel(rhythm, time_sig, category, bar):
+	ms = MeterStructure(time_sig)
+	mWeights = ms.getLHLWeights(1)
+	#print "hierarchy", mWeights
+	num_onsets = 0
+	h_min = min(mWeights)
+
+	syncAbsolute = 0
+	syncNormM = 0
+	syncNormE = 0
+	
+	if 'poly' in category:
+		syncAbsolute = -1
+	else:
+		# segment rhythm into bars
+		rhythm_byBar = subdivide (rhythm, bar)
+
+		def measurePerBar(rhythm):
+			syncopation = 0
+			
+			def aveNeighbours(index, h):
+				averages = []
+				parameter_k = 0.8
+				
+				def findPre(h_hat):
+					pre_index = index - 1
+					while(mWeights[pre_index] < h_hat):
+						pre_index = (pre_index - 1)%len(mWeights)
+					#print "h_hat and pre", h_hat, pre_index
+					return pre_index
+
+				def findPost(h_hat):
+					post_index = (index + 1)%len(mWeights)
+					while(mWeights[post_index] < h_hat):
+						post_index = (post_index + 1)%len(mWeights)
+					#print "h_hat and post", h_hat, post_index
+					return post_index
+				
+				def dif(index1,index2):
+					parameter_beta = 0.5
+					pos1 = int(float(index1)/len(mWeights)*48)
+					pos2 = int(float(index2)/len(mWeights)*48)
+					dif_v = rhythm[pos1]-rhythm[pos2]
+					dif_h = abs(mWeights[index1]-mWeights[index2])
+					dif = dif_v*(parameter_beta*dif_h/4+1-parameter_beta)
+					#print 'dif', dif
+					return dif
+
+				for h_hat in range(h_min,h+1):
+					ave = ( parameter_k*dif(index,findPre(h_hat))+dif(index,findPost(h_hat)) )/(1+parameter_k)
+					#print 'ave', ave
+					averages.append(ave)
+
+				return averages
+
+			for pos in range(len(rhythm)):
+				if rhythm[pos] != 0: # Onset detected
+					#num_onsets += 1
+					i = int((pos/48.0*len(mWeights)))
+					h = mWeights[i]
+					potential = 1 - pow(0.5,(-h))
+					#print "intermediate", min(aveNeighbours(i,h))
+					syncopation += min(aveNeighbours(i, h))*potential
+
+			return syncopation
+
+		for r in rhythm_byBar:
+			syncAbsolute = syncAbsolute + measurePerBar(r)
+		#syncAbsolute /= 2.0
+
+	return syncAbsolute
+
+# Retrieve the stimuli
+#f = file('stimuli.txt')
+#f = file('stimuli_34only.txt')
+f = file('clave.txt')
+
+
+#Calculate syncopation for each rhythm pattern
+#while True:
+for i in range(1):
+	line = f.readline().split(';')
+	if len(line) == 1:
+		break
+	else:
+		sti_name = line[0]
+		rhythmString = line[1].split()
+		time_sig = line[2]
+		category = line[3]
+		bar = int(line[4])
+		
+		rhythm = map(int,rhythmString[0].split(','))
+		print sti_name, sgModel(rhythm, time_sig, category, bar)