fazekasgy@9: '''PySpectralFeatures.py - Example plugin demonstrates''' 
fazekasgy@9: '''how to use the NumPy array interface and write Matlab style code.'''
fazekasgy@9: 
fazekasgy@9: from numpy import *
fazekasgy@9: 
fazekasgy@9: class PySpectralFeatures: 
fazekasgy@9: 	
fazekasgy@9: 	def __init__(self): 
fazekasgy@9: 		self.m_imputSampleRate = 0.0 
fazekasgy@9: 		self.m_stepSize = 0
fazekasgy@9: 		self.m_blockSize = 0
fazekasgy@9: 		self.m_channels = 0
fazekasgy@9: 		self.threshold = 0.00
fazekasgy@9: 		self.r = 2.0
fazekasgy@9: 		
fazekasgy@9: 	def initialise(self,channels,stepSize,blockSize,inputSampleRate):
fazekasgy@9: 		self.m_channels = channels
fazekasgy@9: 		self.m_stepSize = stepSize		
fazekasgy@9: 		self.m_blockSize = blockSize
fazekasgy@9: 		self.m_inputSampleRate = inputSampleRate
fazekasgy@9: 		#self.prevMag = ones((blockSize/2)-1) / ((blockSize/2)-1)
fazekasgy@9: 		self.prevMag = zeros((blockSize/2)-1)
fazekasgy@9: 		self.prevMag[0] = 1
fazekasgy@9: 
fazekasgy@9: 		return True
fazekasgy@9: 	
fazekasgy@9: 	def getMaker(self):
fazekasgy@9: 		return 'VamPy Example Plugins'
fazekasgy@9: 	
fazekasgy@9: 	def getName(self):
fazekasgy@9: 		return 'VamPy Spectral Features'
fazekasgy@9: 		
fazekasgy@9: 	def getIdentifier(self):
fazekasgy@9: 		return 'vampy-sf2'
fazekasgy@9: 
fazekasgy@9: 	def getDescription(self):
fazekasgy@9: 		return 'A collection of low-level spectral descriptors.'
fazekasgy@9: 	
fazekasgy@9: 	def getMaxChannelCount(self):
fazekasgy@9: 		return 1
fazekasgy@9: 		
fazekasgy@9: 	def getInputDomain(self):
fazekasgy@9: 		return 'FrequencyDomain'
fazekasgy@9: 			
fazekasgy@9: 	def getOutputDescriptors(self):
fazekasgy@9: 
fazekasgy@9: 		#descriptors are python dictionaries
fazekasgy@9: 		#Generic values are the same for all
fazekasgy@9: 		Generic={
fazekasgy@9: 		'hasFixedBinCount':True,
fazekasgy@9: 		'binCount':1,
fazekasgy@9: 		'hasKnownExtents':False,
fazekasgy@9: 		'isQuantized':False,
fazekasgy@9: 		'sampleType':'OneSamplePerStep'
fazekasgy@9: 		}
fazekasgy@9: 
fazekasgy@9: 		#Spectral centroid etc...
fazekasgy@9: 		SC=Generic.copy()
fazekasgy@9: 		SC.update({
fazekasgy@9: 		'identifier':'vampy-sc',
fazekasgy@9: 		'name':'Spectral Centroid',
fazekasgy@9: 		'description':'Spectral Centroid (Brightness)',
fazekasgy@9: 		'unit':'Hz'		
fazekasgy@9: 		})
fazekasgy@9: 				
fazekasgy@9: 		SCF=Generic.copy()
fazekasgy@9: 		SCF.update({
fazekasgy@9: 		'identifier':'vampy-scf',
fazekasgy@9: 		'name':'Spectral Crest Factor',
fazekasgy@9: 		'description':'Spectral Crest (Tonality)',
fazekasgy@9: 		'unit':'v'
fazekasgy@9: 		})
fazekasgy@9: 
fazekasgy@9: 		BW=Generic.copy()
fazekasgy@9: 		BW.update({
fazekasgy@9: 		'identifier':'vampy-bw',
fazekasgy@9: 		'name':'Band Width',
fazekasgy@9: 		'description':'Spectral Band Width',
fazekasgy@9: 		'unit':'Hz',
fazekasgy@9: 		})		
fazekasgy@9: 		
fazekasgy@9: 		SE=Generic.copy()
fazekasgy@9: 		SE.update({
fazekasgy@9: 		'identifier':'vampy-se',
fazekasgy@9: 		'name':'Shannon Entropy',
fazekasgy@9: 		'description':'Shannon Entropy',
fazekasgy@9: 		'unit':'',
fazekasgy@9: 		})		
fazekasgy@9: 
fazekasgy@9: 		RE=Generic.copy()
fazekasgy@9: 		RE.update({
fazekasgy@9: 		'identifier':'vampy-re',
fazekasgy@9: 		'name':'Renyi Entropy',
fazekasgy@9: 		'description':'Renyi Entropy',
fazekasgy@9: 		'unit':'',
fazekasgy@9: 		})
fazekasgy@9: 
fazekasgy@9: 		KL=Generic.copy()
fazekasgy@9: 		KL.update({
fazekasgy@9: 		'identifier':'vampy-kl',
fazekasgy@9: 		'name':'Kullback Leibler divergence',
fazekasgy@9: 		'description':'KL divergence between successive spectra',
fazekasgy@9: 		'unit':'',
fazekasgy@9: 		})
fazekasgy@9: 			
fazekasgy@9: 		#return a list of dictionaries
fazekasgy@9: 		return [SC,SCF,BW,SE,RE,KL]
fazekasgy@9: 
fazekasgy@9: 	def getParameterDescriptors(self):
fazekasgy@9: 		threshold={
fazekasgy@9: 		'identifier':'threshold',
fazekasgy@9: 		'name':'Noise threshold: ',
fazekasgy@9: 		'description':'',
fazekasgy@9: 		'unit':'v',
fazekasgy@9: 		'minValue':0.0,
fazekasgy@9: 		'maxValue':0.5,
fazekasgy@9: 		'defaultValue':0.05,
fazekasgy@9: 		'isQuantized':False
fazekasgy@9: 		}
fazekasgy@9: 
fazekasgy@9: 		renyicoeff={
fazekasgy@9: 		'identifier':'r',
fazekasgy@9: 		'name':'Renyi entropy coeff: ',
fazekasgy@9: 		'description':'',
fazekasgy@9: 		'unit':'',
fazekasgy@9: 		'minValue':0.0,
fazekasgy@9: 		'maxValue':10.0,
fazekasgy@9: 		'defaultValue':2,
fazekasgy@9: 		'isQuantized':False		
fazekasgy@9: 		}
fazekasgy@9: 
fazekasgy@9: 		return [threshold,renyicoeff]
fazekasgy@9: 
fazekasgy@9: 	def setParameter(self,paramid,newval):
fazekasgy@9: 		if paramid == 'threshold' :
fazekasgy@9: 			self.threshold = newval
fazekasgy@9: 		if paramid == 'r' :
fazekasgy@9: 			self.r == newval
fazekasgy@9: 		return
fazekasgy@9: 		
fazekasgy@9: 	def getParameter(self,paramid):
fazekasgy@9: 		if paramid == 'threshold' :
fazekasgy@9: 			return self.threshold
fazekasgy@9: 		if paramid == 'r':
fazekasgy@9: 			return float(self.r)
fazekasgy@9: 		else:
fazekasgy@9: 			return 0.0
fazekasgy@9: 			
fazekasgy@9: 	def processN(self,membuffer,samplecount):
fazekasgy@9: 		fftsize = self.m_blockSize
fazekasgy@9: 		sampleRate = self.m_inputSampleRate
fazekasgy@9: 
fazekasgy@9: 		#for time domain plugins use the following line:
fazekasgy@9: 		#audioSamples = frombuffer(membuffer[0],float32)
fazekasgy@9: 		#-1: do till the end, skip DC 2*32bit / 8bit = 8byte
fazekasgy@9: 		complexSpectrum =  frombuffer(membuffer[0],complex64,-1,8)
fazekasgy@9: 		magnitudeSpectrum = abs(complexSpectrum) / (fftsize/2)
fazekasgy@9: 		tpower = sum(magnitudeSpectrum)
fazekasgy@9: 		#phaseSpectrum = angle(complexSpectrum)
fazekasgy@9: 
fazekasgy@9: 		freq = array(range(1,len(complexSpectrum)+1)) \
fazekasgy@9: 		* sampleRate / fftsize
fazekasgy@9: 
fazekasgy@9: 		centroid = 0.0
fazekasgy@9: 		crest = 0.0
fazekasgy@9: 		bw = 0.0
fazekasgy@9: 		shannon = 0.0
fazekasgy@9: 		renyi = 0.0
fazekasgy@9: 		r = self.r
fazekasgy@9: 		KLdiv = 0.0
fazekasgy@9: 		flatness = 0.0
fazekasgy@9: 		exp=1.0 / (fftsize/2)
fazekasgy@9: 		#print exp
fazekasgy@9: 
fazekasgy@9: 		#declare outputs
fazekasgy@9: 		output0=[]
fazekasgy@9: 		output1=[]
fazekasgy@9: 		output2=[]
fazekasgy@9: 		output3=[]
fazekasgy@9: 		output4=[]
fazekasgy@9: 		output5=[]
fazekasgy@9: 		
fazekasgy@9: 		if tpower > self.threshold : 
fazekasgy@9: 
fazekasgy@9: 			centroid = sum(freq * magnitudeSpectrum) / tpower 
fazekasgy@9: 			crest = max(magnitudeSpectrum)  / tpower
fazekasgy@9: 			bw = sum( abs(freq - centroid) * magnitudeSpectrum ) / tpower
fazekasgy@9: 			#flatness = prod(abs(complexSpectrum))  
fazekasgy@9: 			#print flatness
fazekasgy@9: 			normMag = magnitudeSpectrum / tpower #make it sum to 1			
fazekasgy@9: 			shannon = - sum( normMag * log2(normMag) )
fazekasgy@9: 			renyi = (1/1-r) * log10( sum( power(normMag,r)))
fazekasgy@9: 			KLdiv = sum( normMag * log2(normMag / self.prevMag) )
fazekasgy@9: 			self.prevMag = normMag
fazekasgy@9:  				
fazekasgy@9: 		output0.append({
fazekasgy@9: 		'hasTimestamp':False,		
fazekasgy@9: 		'values':[float(centroid)],		
fazekasgy@9: 		#'label':str(centroid)				
fazekasgy@9: 		})
fazekasgy@9: 
fazekasgy@9: 		output1.append({
fazekasgy@9: 		'hasTimestamp':False,		
fazekasgy@9: 		'values':[float(crest)],		
fazekasgy@9: 		#'label':str(crest)				
fazekasgy@9: 		})
fazekasgy@9: 	
fazekasgy@9: 		output2.append({
fazekasgy@9: 		'hasTimestamp':False,		
fazekasgy@9: 		'values':[float(bw)],		
fazekasgy@9: 		#'label':str(bw)				
fazekasgy@9: 		})
fazekasgy@9: 
fazekasgy@9: 		output3.append({
fazekasgy@9: 		'hasTimestamp':False,		
fazekasgy@9: 		'values':[float(shannon)],		
fazekasgy@9: 		#'label':str(shannon)				
fazekasgy@9: 		})
fazekasgy@9: 
fazekasgy@9: 		output4.append({
fazekasgy@9: 		'hasTimestamp':False,		
fazekasgy@9: 		'values':[float(renyi)],		
fazekasgy@9: 		#'label':str(renyi)				
fazekasgy@9: 		})
fazekasgy@9: 
fazekasgy@9: 		output5.append({
fazekasgy@9: 		'hasTimestamp':False,		
fazekasgy@9: 		'values':[float(KLdiv)],		#strictly must be a list
fazekasgy@9: 		#'label':str(renyi)				
fazekasgy@9: 		})
fazekasgy@9: 
fazekasgy@9: 		#return a LIST of list of dictionaries
fazekasgy@9: 		return [output0,output1,output2,output3,output4,output5]