def main() :
import numpy
import math
import sys, os
import RandomArray
sys.path.append(os.path.join(os.path.dirname(__file__),"../../../src/libs/python"))
import bmaudio
# Simulation of random error
speakerDistances = numpy.random.normal(loc=0,scale=standardDelay,size=nSpeakers)
simulatedSpectrum = numpy.array( [
sum( complex(math.cos(w*delay), math.sin(w*delay)) for delay in speakerDistances )
for w in ( math.pi*2*wavenumber*spectralRange/nBins for wavenumber in xrange(nBins) )
])
simulatedSpectrum/=nSpeakers
simulatedInverse = 1/simulatedSpectrum
simulatedInverseAudio = numpy.fft.irfft(simulatedInverse)
# Analytical computation
computedSpectrum = numpy.array([
math.exp(-(standardDelay*w)**2/2)
for w in (2*math.pi*wavenumber*spectralRange/nBins for wavenumber in xrange(nBins) )
])
#computedSpectrum[abs(computedSpectrum)<.5e-2]=.5e-2
computedInverseAudio = numpy.fft.irfft(1/computedSpectrum)
bmaudio.saveWave("lala.wav",computedInverseAudio,44100)
fixedSpectrum = numpy.array([
math.sqrt(w)/10000+math.exp(-(standardDelay*w)**2/2)
for w in (2*math.pi*wavenumber*spectralRange/nBins for wavenumber in xrange(nBins) ) ])
import Gnuplot
gp=Gnuplot.Gnuplot(persist=1)
gp('set data style lines')
gp("set log y ")
gp.plot(
numpy.zeros(nBins),
abs(simulatedSpectrum),
abs(computedSpectrum)[abs(computedSpectrum)>1e-4],
abs(fixedSpectrum),
abs(1/simulatedSpectrum),
abs(1/computedSpectrum)[abs(computedSpectrum)>1e-4],
abs(1/fixedSpectrum),
# [bin.real for bin in spectrum],
# [bin.imag for bin in spectrum],
)
gp.hardcopy(filename="IncoherenceSimulation.png",terminal="png")
gp = bmaudio.SpectrumDisplay()
filter = abs(numpy.fft.irfft(1/exponential))[:nBins]
gp.inDb()
for spectrum, name in [
(numpy.zeros(nBins), ""),
(abs(Ew), "Ew"),
(abs(Ex), "Ex"),
(abs(Ey), "Ey"),
(abs(Ez), "Ez"),
(abs(Ew)/exponential, "Fixed Ew"),
(abs(Ex)/exponential, "Fixed Ex"),
(abs(Ey)/exponential, "Fixed Ey"),
(abs(Ez)/exponential, "Fixed Ez"),
(Iwsum, "Iwsum"),
(abs(Ew)/Iwsum, "Ew/Iwsum"), # The ratio
# [bin.real for bin in spectrum],
# [bin.imag for bin in spectrum],
(exponential, "Fix filter"),
] :
gp.addSpectrumData(spectrum, 256, name)
gp.show()
gp.hardcopy("IncoherenceSimulation.png","png")
print filter/max(filter)
bmaudio.saveWave("coherenceFilter.wav", filter/max(filter), 44100)
#bmaudio.saveWave("lala.wav",computedInverseAudio,44100)
print len(azimuths)
print elevations
print azimuths
for matfile in files :
matdata = scipy.io.loadmat(matfile)
subject = re.findall('[0-9]+',matdata['name'][0])[0] # has the form subject_000
os.system('mkdir -p "CIPIC/subject_%s"' % subject)
for channel in ['l','r'] :
database=open("cipic%s%s.hrtfs"%(subject,channel.upper()),'w')
hrirs = matdata['hrir_'+channel]
normFactor = max(abs(hrirs.reshape(hrirs.size)))
hrirs /= normFactor
for azimuthIndex, azimuthData in enumerate(hrirs) :
for elevationIndex, hrir in enumerate(azimuthData) :
azimuth = azimuths[azimuthIndex]
elevation = elevations[elevationIndex]
print "Subject", subject, "original", elevation, azimuth, "->",
if elevation > 90 :
azimuth = 180 - azimuth
elevation = 180 - elevation
if channel == 'l' : azimuth = (360 - azimuth)%360
print elevation, azimuth
wavefile = 'CIPIC/subject_%s/%s_e%+03.1f_a%+03i.wav'%(subject,channel.upper(),elevation,azimuth)
print >> database, elevation, azimuth, wavefile
bmaudio.saveWave(wavefile, hrir, 44100)
