view Example VamPy plugins/PyMFCC.py @ 37:27bab3a16c9a vampy2final

new branch Vampy2final

'''PyMFCC.py - This example Vampy plugin demonstrates
how to return sprectrogram-like features and how to return
data using the getRemainingFeatures() function.

The plugin has frequency domain input and is using the
numpy array interface. (Flag: vf_ARRAY)

Outputs: 
1) 2-128 MFCC coefficients
2) Mel-warped spectrum used for the MFCC computation
3) Filter matrix used for Mel scaling

Centre for Digital Music, Queen Mary University of London.
Copyright (C) 2009 Gyorgy Fazekas, QMUL. (See Vamp sources 
for licence information.)

Constants for Mel frequency conversion and filter 
centre calculation are taken from the GNU GPL licenced 
Freespeech library. Copyright (C) 1999 Jean-Marc Valin
'''

import sys,numpy,vampy
from numpy import abs,log,exp,floor,sum,sqrt,cos,hstack
from numpy.fft import *
from vampy import *


class melScaling(object):

	def __init__(self,sampleRate,inputSize,numBands,minHz = 0,maxHz = None):
		'''Initialise frequency warping and DCT matrix. 
		Parameters:
		sampleRate: audio sample rate
		inputSize: length of magnitude spectrum (half of FFT size assumed)
		numBands: number of mel Bands (MFCCs)
		minHz: lower bound of warping  (default = DC)
		maxHz: higher bound of warping (default = Nyquist frequency)
		'''
		self.sampleRate = sampleRate
		self.NqHz = sampleRate / 2.0
		self.minHz = minHz
		if maxHz is None : maxHz = self.NqHz
		self.maxHz = maxHz
		self.inputSize = inputSize
		self.numBands = numBands
		self.valid = False
		self.updated = False
		
	def update(self): 
		# make sure this will run only once 
		# if called from a vamp process
		if self.updated: return self.valid
		self.updated = True
		self.valid = False
		print 'Updating parameters and recalculating filters: '
		print 'Nyquist: ',self.NqHz
		
		if self.maxHz > self.NqHz : 
			raise Exception('Maximum frequency must be smaller than the Nyquist frequency')
		
		self.maxMel = 1000*log(1+self.maxHz/700.0)/log(1+1000.0/700.0)
		self.minMel = 1000*log(1+self.minHz/700.0)/log(1+1000.0/700.0)
		print 'minHz:%s\nmaxHz:%s\nminMel:%s\nmaxMel:%s\n' \
		%(self.minHz,self.maxHz,self.minMel,self.maxMel)
		self.filterMatrix = self.getFilterMatrix(self.inputSize,self.numBands)
		self.DCTMatrix = self.getDCTMatrix(self.numBands)
		self.filterIter = self.filterMatrix.__iter__()
		self.valid = True
		return self.valid
				
	def getFilterCentres(self,inputSize,numBands):
		'''Calculate Mel filter centres around FFT bins.
		This function calculates two extra bands at the edges for
		finding the starting and end point of the first and last 
		actual filters.'''
		centresMel = numpy.array(xrange(numBands+2)) * (self.maxMel-self.minMel)/(numBands+1) + self.minMel
		centresBin = numpy.floor(0.5 + 700.0*inputSize*(exp(centresMel*log(1+1000.0/700.0)/1000.0)-1)/self.NqHz)
		return numpy.array(centresBin,int)
		
	def getFilterMatrix(self,inputSize,numBands):
		'''Compose the Mel scaling matrix.'''
		filterMatrix = numpy.zeros((numBands,inputSize))
		self.filterCentres = self.getFilterCentres(inputSize,numBands)
		for i in xrange(numBands) :
			start,centre,end = self.filterCentres[i:i+3]
			self.setFilter(filterMatrix[i],start,centre,end)
		return filterMatrix.transpose()

	def setFilter(self,filt,filterStart,filterCentre,filterEnd):
		'''Calculate a single Mel filter.'''
		k1 = numpy.float32(filterCentre-filterStart)
		k2 = numpy.float32(filterEnd-filterCentre)
		up = (numpy.array(xrange(filterStart,filterCentre))-filterStart)/k1
		dn = (filterEnd-numpy.array(xrange(filterCentre,filterEnd)))/k2
		filt[filterStart:filterCentre] = up
		filt[filterCentre:filterEnd] = dn
						
	def warpSpectrum(self,magnitudeSpectrum):
		'''Compute the Mel scaled spectrum.'''
		return numpy.dot(magnitudeSpectrum,self.filterMatrix)
		
	def getDCTMatrix(self,size):
		'''Calculate the square DCT transform matrix. Results are 
		equivalent to Matlab dctmtx(n) with 64 bit precision.'''
		DCTmx = numpy.array(xrange(size),numpy.float64).repeat(size).reshape(size,size)
		DCTmxT = numpy.pi * (DCTmx.transpose()+0.5) / size
		DCTmxT = (1.0/sqrt( size / 2.0)) * cos(DCTmx * DCTmxT)
		DCTmxT[0] = DCTmxT[0] * (sqrt(2.0)/2.0)
		return DCTmxT
		
	def dct(self,data_matrix):
		'''Compute DCT of input matrix.'''
		return numpy.dot(self.DCTMatrix,data_matrix)
		
	def getMFCCs(self,warpedSpectrum,cn=True):
		'''Compute MFCC coefficients from Mel warped magnitude spectrum.'''
		mfccs=self.dct(numpy.log(warpedSpectrum))
		if cn is False : mfccs[0] = 0.0
		return mfccs
	

class PyMFCC(melScaling): 
	
	def __init__(self,inputSampleRate):
		
		# flags for setting some Vampy options
		self.vampy_flags = vf_DEBUG | vf_ARRAY | vf_REALTIME

		self.m_inputSampleRate = int(inputSampleRate)
		self.m_stepSize = 1024
		self.m_blockSize = 2048
		self.m_channels = 1
		self.numBands = 40
		self.cnull = 1
		self.two_ch = False
		melScaling.__init__(self,int(self.m_inputSampleRate),self.m_blockSize/2,self.numBands)
		
	def initialise(self,channels,stepSize,blockSize):
		self.m_channels = channels
		self.m_stepSize = stepSize		
		self.m_blockSize = blockSize
		self.window = numpy.hamming(blockSize)
		melScaling.__init__(self,int(self.m_inputSampleRate),self.m_blockSize/2,self.numBands)
		return True
	
	def getMaker(self):
		return 'Vampy Example Plugins'
		
	def getCopyright(self):
		return 'Plugin By George Fazekas'
	
	def getName(self):
		return 'Vampy MFCC Plugin'
		
	def getIdentifier(self):
		return 'vampy-mfcc'

	def getDescription(self):
		return 'A simple MFCC plugin'
	
	def getMaxChannelCount(self):
		return 2
		
	def getInputDomain(self):
		return FrequencyDomain #TimeDomain
		
	def getPreferredBlockSize(self):
		return 2048
		
	def getPreferredStepSize(self):
		return 1024
			
	def getOutputDescriptors(self):
		
		Generic = OutputDescriptor() 
		Generic.hasFixedBinCount=True
		Generic.binCount=int(self.numBands)-self.cnull
		Generic.hasKnownExtents=False
		Generic.isQuantized=True
		Generic.sampleType = OneSamplePerStep 
		
		# note the inheritance of attributes (optional)
		MFCC = OutputDescriptor(Generic)
		MFCC.identifier = 'mfccs'
		MFCC.name = 'MFCCs'
		MFCC.description = 'MFCC Coefficients'
		MFCC.binNames=map(lambda x: 'C '+str(x),range(self.cnull,int(self.numBands)))
		if self.two_ch and self.m_channels == 2 :
			MFCC.binNames *= 2 #repeat the list
		MFCC.unit = None
		if self.two_ch and self.m_channels == 2 :
			MFCC.binCount = self.m_channels * (int(self.numBands)-self.cnull)
		else :
			MFCC.binCount = self.numBands-self.cnull
				
		warpedSpectrum = OutputDescriptor(Generic)
		warpedSpectrum.identifier='warped-fft'
		warpedSpectrum.name='Mel Scaled Spectrum'
		warpedSpectrum.description='Mel Scaled Magnitide Spectrum'
		warpedSpectrum.unit='Mel'
		if self.two_ch and self.m_channels == 2 :
			warpedSpectrum.binCount = self.m_channels * int(self.numBands) 
		else :
			warpedSpectrum.binCount = self.numBands
		
		melFilter = OutputDescriptor(Generic)
		melFilter.identifier = 'mel-filter-matrix'
		melFilter.sampleType='FixedSampleRate'
		melFilter.sampleRate=self.m_inputSampleRate/self.m_stepSize
		melFilter.name='Mel Filter Matrix'
		melFilter.description='Returns the created filter matrix in getRemainingFeatures.'
		melFilter.unit = None
				
		return OutputList(MFCC,warpedSpectrum,melFilter)
		

	def getParameterDescriptors(self):

		melbands = ParameterDescriptor()
		melbands.identifier='melbands'
		melbands.name='Number of bands (coefficients)'
		melbands.description='Set the number of coefficients.'
		melbands.unit = ''
		melbands.minValue = 2
		melbands.maxValue = 128
		melbands.defaultValue = 40
		melbands.isQuantized = True
		melbands.quantizeStep = 1
				
		cnull = ParameterDescriptor()
		cnull.identifier='cnull'
		cnull.name='Return C0'
		cnull.description='Select if the DC coefficient is required.'
		cnull.unit = None
		cnull.minValue = 0
		cnull.maxValue = 1
		cnull.defaultValue = 0
		cnull.isQuantized = True
		cnull.quantizeStep = 1
		
		two_ch = ParameterDescriptor(cnull)
		two_ch.identifier='two_ch'
		two_ch.name='Process channels separately'
		two_ch.description='Process two channel files separately.'
		two_ch.defaultValue = False
				
		minHz = ParameterDescriptor()
		minHz.identifier='minHz'
		minHz.name='minimum frequency'
		minHz.description='Set the lower frequency bound.'
		minHz.unit='Hz'
		minHz.minValue = 0
		minHz.maxValue = 24000
		minHz.defaultValue = 0
		minHz.isQuantized = True
		minHz.quantizeStep = 1.0
		
		maxHz = ParameterDescriptor()
		maxHz.identifier='maxHz'
		maxHz.description='Set the upper frequency bound.'
		maxHz.name='maximum frequency'
		maxHz.unit='Hz'
		maxHz.minValue = 100
		maxHz.maxValue = 24000
		maxHz.defaultValue = 11025
		maxHz.isQuantized = True
		maxHz.quantizeStep = 100
		
		return ParameterList(melbands,minHz,maxHz,cnull,two_ch)
		

	def setParameter(self,paramid,newval):
		self.valid = False
		if paramid == 'minHz' :
			if newval < self.maxHz and newval < self.NqHz :
				self.minHz = float(newval)
		if paramid == 'maxHz' :
			if newval < self.NqHz and newval > self.minHz+1000 :
				self.maxHz = float(newval)
			else :
				self.maxHz = self.NqHz
		if paramid == 'cnull' :
			self.cnull = int(not int(newval))
		if paramid == 'melbands' :
			self.numBands = int(newval)
		if paramid == 'two_ch' :
			self.two_ch = bool(newval)
		return None

				
	def getParameter(self,paramid):
		if paramid == 'minHz' :
			return self.minHz
		if paramid == 'maxHz' :
			return self.maxHz
		if paramid == 'cnull' :
			return bool(not int(self.cnull))
		if paramid == 'melbands' :
			return self.numBands
		if paramid == 'two_ch' :
			return self.two_ch
		else:
			return 0.0

	# set numpy array process using the 'vf_ARRAY' flag in __init__()
	# and RealTime time stamps using the 'vf_REALTIME' flag
	def process(self,inputbuffers,timestamp):
		
		# calculate the filter and DCT matrices, check 
		# if they are computable given a set of parameters
		# (we only do this once, when the process is called first)
		if not self.update() : return None
		
		# if two channel processing is set, use process2ch
		if self.m_channels == 2 and self.two_ch :
			return self.process2ch(inputbuffers,timestamp)
		
		fftsize = self.m_blockSize
		
		if self.m_channels > 1 :
			# take the average of two magnitude spectra
			mS0 = abs(inputbuffers[0])[0:fftsize/2]
			mS1 = abs(inputbuffers[1])[0:fftsize/2]
			magnitudeSpectrum = (mS0 + mS1) / 2
		else :
			complexSpectrum = inputbuffers[0]
			magnitudeSpectrum = abs(complexSpectrum)[0:fftsize/2]

		# do the frequency warping and MFCC computation
		melSpectrum = self.warpSpectrum(magnitudeSpectrum)
		melCepstrum = self.getMFCCs(melSpectrum,cn=True)
		
		# returning the values: 
		outputs = FeatureSet()
		
		# 1) full initialisation example using a FeatureList
		f_mfccs = Feature()
		f_mfccs.values = melCepstrum[self.cnull:]
		outputs[0] = FeatureList(f_mfccs)
		
		# 2) simplified: when only one feature is required, 
		# the FeatureList() can be omitted
		outputs[1] = Feature(melSpectrum)
		
		# this is equivalint to writing :
		# outputs[1] = Feature()
		# outputs[1].values = melSpectrum
		# or using keyword args: Feature(values = melSpectrum)
			
		return outputs
		
	# process channels separately (stack the returned arrays)
	def process2ch(self,inputbuffers,timestamp):

		fftsize = self.m_blockSize
		
		complexSpectrum0 = inputbuffers[0]
		complexSpectrum1 = inputbuffers[1]
		
		magnitudeSpectrum0 = abs(complexSpectrum0)[0:fftsize/2]
		magnitudeSpectrum1 = abs(complexSpectrum1)[0:fftsize/2]
		
		# do the computations
		melSpectrum0 = self.warpSpectrum(magnitudeSpectrum0)
		melCepstrum0 = self.getMFCCs(melSpectrum0,cn=True)
		melSpectrum1 = self.warpSpectrum(magnitudeSpectrum1)
		melCepstrum1 = self.getMFCCs(melSpectrum1,cn=True)
		
		outputs = FeatureSet()
		outputs[0] = Feature(hstack((melCepstrum1[self.cnull:],melCepstrum0[self.cnull:])))
		outputs[1] = Feature(hstack((melSpectrum1,melSpectrum0)))
		
		return outputs
	

	def getRemainingFeatures(self):
		if not self.update() : return []
		frameSampleStart = 0
		
		output_featureSet = FeatureSet()

		# the filter is the third output (index starts from zero)
		output_featureSet[2] = flist = FeatureList()

		while True:
			f = Feature()
			f.hasTimestamp = True
			f.timestamp = frame2RealTime(frameSampleStart,self.m_inputSampleRate)
			try :
				f.values = self.filterIter.next()
			except StopIteration :
				break
			flist.append(f)
			frameSampleStart += self.m_stepSize

		return output_featureSet