diff Syncopation models/TMC.py @ 0:76ce27beba95

Have uploaded the syncopation dataset and audio wavefies.

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/Syncopation models/TMC.py	Fri Mar 21 15:49:46 2014 +0000
@@ -0,0 +1,72 @@
+'''
+Author: Chunyang Song
+Institution: Centre for Digital Music, Queen Mary University of London
+
+** Toussaint's Metric Complexity Model **
+
+Algorithm:
+
+Only applicable to monorhythms.
+
+Define metrical hierarchy by given time signature;
+Calculate how many onsets and determine Maximum Metricality Max_Metric;
+Calculate the Metrical Simplicity - the weights of all onsets;
+Syncopation = Max_Metric - Metric_simplicity.
+Output the predicted syncopation score; -1 indicates non-applicable
+
+'''
+
+from MeterStructure import MeterStructure
+
+def metricalModel(rhythm, time_sig, category, bar):
+	ms = MeterStructure(time_sig)
+	meter = ms.getLJWeights(bar)
+	if len(meter) !=0:
+		
+		metricalSimplicity = 0  # sum of weights of onsets per bar
+		maxMetrical = 0 	 # maximum metricity per bar
+		onsetCount = 0		 # The number of onsets per bar
+	
+		if 'poly' in category:  # not applicable to polyrhythms
+			return -1
+	
+		# Calculate metricalSimplicity
+		else:
+			l = len(rhythm)
+			for i in range(l):
+				if rhythm[i] == 1: # onset detected
+					pos = int((float(i)/l) *len(meter)) # looking for the metrical position where this note locates
+					metricalSimplicity = metricalSimplicity+meter[pos]
+					onsetCount = onsetCount+1
+		
+			# Calculate max metricity
+			meter.sort(reverse=True)
+			for i in range(0,onsetCount):
+				maxMetrical = maxMetrical+meter[i]
+			
+			#print 'test', onsetCount, maxMetrical, metricalSimplicity
+			syncopation = (maxMetrical - metricalSimplicity)
+	
+			return syncopation	
+	else:
+		return -1
+
+# Retrieve the stimuli
+f = file('stimuli.txt')
+#f = file('stimuli_34only.txt')
+
+#Calculate syncopation for each rhythm pattern
+while True:
+	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, metricalModel(rhythm, time_sig, category, bar)