fazekasgy@52: '''PySpectralCentroid.py - Example plugin demonstrates 
fazekasgy@52: how to write a C style plugin using VamPy in pure Python.
fazekasgy@52: This plugin also introduces the use of the builtin vampy
fazekasgy@52: extension module.
fazekasgy@52: 
fazekasgy@52: The plugin has frequency domain input and is using the
Chris@69: legacy interface: the FFT output is passed as a list
fazekasgy@52: of complex numbers.
fazekasgy@52: 
fazekasgy@52: Outputs: 
fazekasgy@52: 1) Spectral centroid
fazekasgy@52: 
fazekasgy@52: Note: This is not the adviced way of writing Vampy plugins now,
fazekasgy@52: since the interfaces provided for Numpy are at least 5 times
fazekasgy@52: faster. However, this is still a nice and easy to understand
fazekasgy@52: example, which also shows how can one write a reasonable
fazekasgy@52: plugin without having Numpy installed.
fazekasgy@52: 
fazekasgy@52: Warning: Earlier versions of this plugin are now obsolete.
fazekasgy@52: (They were using the legacy interface of Vampy 1 which
fazekasgy@52: did not distinquish between time and frequency domain inputs.)
fazekasgy@52: 
fazekasgy@52: Centre for Digital Music, Queen Mary University of London.
fazekasgy@52: Copyright (C) 2009 Gyorgy Fazekas, QMUL. (See Vamp sources 
fazekasgy@52: for licence information.)
fazekasgy@52: 
fazekasgy@52: '''
fazekasgy@52: 
fazekasgy@52: # import the names we use from vampy 
fazekasgy@52: from vampy import Feature,FeatureSet,ParameterDescriptor
fazekasgy@52: from vampy import OutputDescriptor,FrequencyDomain,OneSamplePerStep
fazekasgy@52: 
fazekasgy@52: from math import sqrt
fazekasgy@52: 
fazekasgy@52: class PySpectralCentroid: 
fazekasgy@52: 	
fazekasgy@52: 	def __init__(self,inputSampleRate): 
fazekasgy@52: 		self.m_stepSize = 0
fazekasgy@52: 		self.m_blockSize = 0
fazekasgy@52: 		self.m_channels = 0
fazekasgy@52: 		self.previousSample = 0.0
fazekasgy@52: 		self.m_inputSampleRate = inputSampleRate
Chris@68: 		self.threshold = 0.05
fazekasgy@52: 		
fazekasgy@52: 	def initialise(self,channels,stepSize,blockSize):
fazekasgy@52: 		self.m_channels = channels
fazekasgy@52: 		self.m_stepSize = stepSize		
fazekasgy@52: 		self.m_blockSize = blockSize
fazekasgy@52: 		return True
fazekasgy@52: 	
fazekasgy@52: 	def getMaker(self):
fazekasgy@52: 		return 'Vampy Example Plugins'
fazekasgy@52: 	
fazekasgy@52: 	def getName(self):
fazekasgy@52: 		return 'Spectral Centroid (using legacy process interface)'
fazekasgy@52: 		
fazekasgy@52: 	def getIdentifier(self):
fazekasgy@52: 		return 'vampy-sc3'
Chris@69: 
Chris@69:         def getDescription(self):
Chris@69:                 return 'Calculate the linear frequency centroid of the short-time Fourier spectrum'
Chris@69: 
Chris@69:         def getCopyright(self):
Chris@69:                 return 'Plugin By George Fazekas. Freely redistributable example plugin (BSD license)'
Chris@69:         
fazekasgy@52: 	def getMaxChannelCount(self):
fazekasgy@52: 		return 1
fazekasgy@52: 		
fazekasgy@52: 	def getInputDomain(self):
fazekasgy@52: 		return FrequencyDomain
fazekasgy@52: 			
fazekasgy@52: 	def getOutputDescriptors(self):
fazekasgy@52: 		
fazekasgy@52: 		cod = OutputDescriptor()
fazekasgy@52: 		cod.identifier='vampy-sc3'
fazekasgy@52: 		cod.name='Spectral Centroid'
fazekasgy@52: 		cod.description='Spectral Centroid (Brightness)'
fazekasgy@52: 		cod.unit=''
fazekasgy@52: 		cod.hasFixedBinCount=True
fazekasgy@52: 		cod.binCount=1
fazekasgy@52: 		cod.hasKnownExtents=False
fazekasgy@52: 		cod.isQuantized=True
fazekasgy@52: 		cod.quantizeStep=1.0
fazekasgy@52: 		cod.sampleType=OneSamplePerStep
fazekasgy@52: 		return cod
fazekasgy@52: 
fazekasgy@52: 	def getParameterDescriptors(self):
fazekasgy@52: 		thd = ParameterDescriptor()
fazekasgy@52: 		thd.identifier='threshold'
fazekasgy@52: 		thd.name='Noise threshold'
Chris@68: 		thd.description='Magnitude below which a process block will be disregarded and zero returned'
fazekasgy@52: 		thd.unit='v'
fazekasgy@52: 		thd.minValue=0.0
fazekasgy@52: 		thd.maxValue=0.5
fazekasgy@52: 		thd.defaultValue=0.05
fazekasgy@52: 		thd.isQuantized=False
fazekasgy@52: 		return thd
fazekasgy@52: 
fazekasgy@52: 	def setParameter(self,paramid,newval):
fazekasgy@52: 		if paramid == 'threshold' :
fazekasgy@52: 			self.threshold = newval
fazekasgy@52: 		return
fazekasgy@52: 		
fazekasgy@52: 	def getParameter(self,paramid):
fazekasgy@52: 		if paramid == 'threshold' :
fazekasgy@52: 			return self.threshold
fazekasgy@52: 		else:
fazekasgy@52: 			return 0.0
fazekasgy@52: 			
fazekasgy@52: 	def process(self,inputbuffers,timestamp):
fazekasgy@52: 		
fazekasgy@52: 		# this is a 1 channel frequency domain plugin, therefore
fazekasgy@52: 		# inputbuffers contain (block size / 2) + 1 complex numbers
fazekasgy@52: 		# corresponding to the FFT output from DC to Nyquist inclusive
fazekasgy@52: 		
fazekasgy@52: 		cplxArray = inputbuffers[0][:-1]
fazekasgy@52: 
fazekasgy@52: 		prev = self.previousSample
fazekasgy@52: 		numLin = 0.0
fazekasgy@52: 		denom = 0.0
fazekasgy@52: 		centroid = 0.0		
fazekasgy@52: 
fazekasgy@52: 		output = FeatureSet()
fazekasgy@52: 
fazekasgy@52: 		pw = 0
fazekasgy@52: 		for i in xrange(1,len(cplxArray)) : 
fazekasgy@52: 			pw = pw + abs(cplxArray[i])
fazekasgy@52: 		
fazekasgy@52: 		if pw > self.threshold : 
fazekasgy@52: 			for i in range(1,(len(cplxArray))) :
fazekasgy@52: 				
fazekasgy@52: 				re = cplxArray[i].real
fazekasgy@52: 				im = cplxArray[i].imag
fazekasgy@52: 				freq = i * self.m_inputSampleRate / self.m_blockSize
fazekasgy@52: 				power = sqrt (re*re + im*im) / (self.m_blockSize/2)
fazekasgy@52: 				denom = denom + power
fazekasgy@52: 				numLin = numLin + freq * power
fazekasgy@52: 				
fazekasgy@52: 			if denom != 0 :
fazekasgy@52: 				centroid = numLin / denom 
fazekasgy@52: 				
fazekasgy@52: 		else :
fazekasgy@52: 			centroid = 0.0
fazekasgy@52: 			
fazekasgy@52: 		output[0] = Feature()
fazekasgy@52: 		output[0].values = centroid
fazekasgy@52: 		output[0].label = str(centroid)
fazekasgy@52: 		
fazekasgy@52: 		return output