comparison Example VamPy plugins/PySpectralFeatures.py @ 9:d2d36e7d2276

more examples and some bug fixes

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