def plot_freqz(b,
a,
w=None,
npoints=None,
title='',
db=False,
createFigure=True,
label=''):
# Create the omega array if necessary
if npoints is None:
npoints = 1000
if w is None:
w = scipy.arange(-scipy.pi, scipy.pi, 2 * scipy.pi / (npoints))
# Calculate the frequency response
d = loudia.freqz(b.T, a.T, w)
if db:
mag = 20.0 * scipy.log10(abs(d[:, 0]))
else:
mag = abs(d[:, 0])
import pylab
if createFigure:
pylab.figure()
pylab.subplot(2, 1, 1)
pylab.plot(w, mag, label=label)
pylab.title('%s \n Magnitude of the Frequency Response' % title)
pylab.subplot(2, 1, 2)
pylab.plot(w, scipy.angle(d[:, 0]), label=label)
pylab.title('Angle of the Frequency Response')
def plot_freqz(b, a, w = None, npoints = None, title = '', db = False, createFigure = True, label = ''):
# Create the omega array if necessary
if npoints is None:
npoints = 1000
if w is None:
w = scipy.arange(-scipy.pi, scipy.pi, 2*scipy.pi/(npoints))
# Calculate the frequency response
d = loudia.freqz(b.T, a.T, w)
if db:
mag = 20.0 * scipy.log10(abs(d[:,0]))
else:
mag = abs(d[:,0])
import pylab
if createFigure:
pylab.figure()
pylab.subplot(2,1,1)
pylab.plot(w, mag, label = label)
pylab.title('%s \n Magnitude of the Frequency Response' % title)
pylab.subplot(2,1,2)
pylab.plot(w, scipy.angle(d[:,0]), label = label)
pylab.title('Angle of the Frequency Response')
# -------------------------------------------------------- #
import loudia
coeffsB1 = scipy.vstack((B0, B11, B2)) / gain
coeffsB2 = scipy.vstack((B0, B12, B2))
coeffsB3 = scipy.vstack((B0, B13, B2))
coeffsB4 = scipy.vstack((B0, B14, B2))
coeffsA = scipy.vstack((A0, A1, A2))
npoints = 10000
w = scipy.arange(-scipy.pi, scipy.pi, 2*scipy.pi/npoints)
d1 = loudia.freqz(coeffsB1, coeffsA, w)
d2 = loudia.freqz(coeffsB2, coeffsA, w)
d3 = loudia.freqz(coeffsB3, coeffsA, w)
d4 = loudia.freqz(coeffsB4, coeffsA, w)
d = d1 * d2 * d3 * d4
# Plot the frequency response
import pylab
subplots = 2 if plotAngle else 1;
pylab.subplot(subplots,1,1)
if plotColor:
pylab.plot(w[npoints/2:], loudia.magToDb(abs(d[npoints/2:,:])))
import scipy
# Create the omega array
npoints = 1000
w = scipy.arange(-scipy.pi, scipy.pi, 2.0 * scipy.pi / npoints)
# Create the coefficients of the filter
nbcoeffs = 10
b = scipy.ones((nbcoeffs, 1)) / nbcoeffs
a = scipy.ones((1, 1))
print b
print a
print w
# Calculate the frequency response
d = loudia.freqz(b, a, w)
# Plot the frequency response
import pylab
pylab.subplot(2, 1, 1)
pylab.plot(w, abs(d[:, 0]))
pylab.title('Magnitude of the Frequency Response')
pylab.subplot(2, 1, 2)
pylab.plot(w, scipy.angle(d[:, 0]))
pylab.title('Angle of the Frequency Response')
pylab.show()
if interactivePlot:
pylab.ion()
pylab.figure()
pylab.title('Interactive plot of the FFT vs LPC frequency response')
pylab.gca().set_ylim([-100, 40])
pylab.gca().set_autoscale_on(False)
for frame in stream:
fft = ffter.process( windower.process( frame ) )[0, :]
spec = loudia.magToDb( abs( fft ) )
lpcCoeffs, reflection, error = lpc.process( frame )
lpcResidual = lpcr.process( frame, lpcCoeffs )
freqResp = loudia.magToDb( abs( loudia.freqz( b * scipy.sqrt( abs( error[0] ) ), lpcCoeffs.T, w ) ).T )
if interactivePlot:
pylab.subplot( 211 )
pylab.hold( False )
pylab.plot( frame )
#pylab.hold( True )
#pylab.plot( lpcResidual[0,:] )
pylab.subplot( 212 )
pylab.hold( False )
pylab.plot( w, spec[0, :], label = 'FFT' )
pylab.hold( True )
pylab.plot( w, freqResp[0, :], label = 'LPC' )
specs.append( spec[0, :plotSize] )
import scipy
# Create the omega array
npoints = 1000
w = scipy.arange(-scipy.pi, scipy.pi, 2.0*scipy.pi/npoints)
# Create the coefficients of the filter
nbcoeffs = 10
b = scipy.ones((nbcoeffs, 1)) / nbcoeffs
a = scipy.ones((1, 1))
print b
print a
print w
# Calculate the frequency response
d = loudia.freqz(b, a, w)
# Plot the frequency response
import pylab
pylab.subplot(2,1,1)
pylab.plot(w, abs(d[:,0]))
pylab.title('Magnitude of the Frequency Response')
pylab.subplot(2,1,2)
pylab.plot(w, scipy.angle(d[:,0]))
pylab.title('Angle of the Frequency Response')
pylab.show()
gain) = filter_coeffs_gammatone(freqs, B, sampleRate)
# -------------------------------------------------------- #
import loudia
coeffsB1 = scipy.vstack((B0, B11, B2)) / gain
coeffsB2 = scipy.vstack((B0, B12, B2))
coeffsB3 = scipy.vstack((B0, B13, B2))
coeffsB4 = scipy.vstack((B0, B14, B2))
coeffsA = scipy.vstack((A0, A1, A2))
npoints = 10000
w = scipy.arange(-scipy.pi, scipy.pi, 2 * scipy.pi / npoints)
d1 = loudia.freqz(coeffsB1, coeffsA, w)
d2 = loudia.freqz(coeffsB2, coeffsA, w)
d3 = loudia.freqz(coeffsB3, coeffsA, w)
d4 = loudia.freqz(coeffsB4, coeffsA, w)
d = d1 * d2 * d3 * d4
# Plot the frequency response
import pylab
subplots = 2 if plotAngle else 1
pylab.subplot(subplots, 1, 1)
if plotColor:
pylab.plot(w[npoints / 2:], loudia.magToDb(abs(d[npoints / 2:, :])))
else:
pylab.ion()
pylab.figure()
pylab.title('Interactive plot of the FFT vs LPC frequency response')
pylab.gca().set_ylim([-100, 40])
pylab.gca().set_autoscale_on(False)
for frame in stream:
fft = ffter.process(windower.process(frame))[0, :]
spec = loudia.magToDb(abs(fft))
lpcCoeffs, reflection, error = lpc.process(frame)
lpcResidual = lpcr.process(frame, lpcCoeffs)
freqResp = loudia.magToDb(
abs(loudia.freqz(b * scipy.sqrt(abs(error[0])), lpcCoeffs.T, w)).T)
if interactivePlot:
pylab.subplot(211)
pylab.hold(False)
pylab.plot(frame)
#pylab.hold( True )
#pylab.plot( lpcResidual[0,:] )
pylab.subplot(212)
pylab.hold(False)
pylab.plot(w, spec[0, :], label='FFT')
pylab.hold(True)
pylab.plot(w, freqResp[0, :], label='LPC')
specs.append(spec[0, :plotSize])
