# See the file COPYING for the licence associated with this software. # # Author(s): # Phil Garner # import numpy as np import matplotlib.pyplot as plt import ssp i = np.identity(30) o1 = ssp.AllPassWarpOppenheim(i, alpha=0, size=40) o2 = ssp.AllPassWarpOppenheim(i, alpha=0.1, size=40) o3 = ssp.AllPassWarpOppenheim(i, alpha=0.3, size=40) #o5 = ssp.AllPassWarpOppenheim(i, alpha=-0.1, size=40) #o6 = ssp.AllPassWarpOppenheim(i, alpha=-0.3, size=40) o5 = ssp.AllPassWarpMatrix(30, alpha=-0.1, size=40) o6 = ssp.AllPassWarpMatrix(30, alpha=-0.3, size=40) #m = ssp.AllPassWarpMatrix(4, alpha=0.42, size=40) fig = plt.figure() o1Mat = fig.add_subplot(2,3,1) o2Mat = fig.add_subplot(2,3,2) o3Mat = fig.add_subplot(2,3,3) o5Mat = fig.add_subplot(2,3,5) o6Mat = fig.add_subplot(2,3,6) o1Mat.imshow(o1.T) o2Mat.imshow(o2.T) o3Mat.imshow(o3.T)
ls = ssp.ARSpectrum(a, g, nSpec=128)
lap("Spectrum")
# Now do some esoteric AR
t = ssp.parameter('AR', 'matrix')
if t == 'matrix':
wa, wg = ssp.ARMatrix(f, order, method=ssp.parameter('Method', 'matrix'))
if t == 'arwarp':
wa, wg = ssp.ARAllPassWarp(a, g, alpha=ssp.mel[pcm.rate])
elif t == 'acwarp':
ac = ssp.Autocorrelation(f)
ac = ssp.AutocorrelationAllPassWarp(ac, alpha=ssp.mel[pcm.rate],
size=order+1)
wa, wg = ssp.ARLevinson(ac, order)
elif t == 'tdwarp':
m = ssp.AllPassWarpMatrix(256, ssp.mel[pcm.rate])
fw = np.dot(f,m.T)
aw = ssp.Autocorrelation(fw)
wa, wg = ssp.ARLevinson(aw, order)
elif t == 'ridge':
ac = ssp.Autocorrelation(f)
wa, wg = ssp.ARRidge(ac, order, ridge=0.01)
elif t == 'lasso':
ac = ssp.Autocorrelation(f)
wa, wg = ssp.ARLasso(ac, order, ridge=30)
elif t == 'sparse':
wa, wg = ssp.ARSparse(f, order, ssp.parameter("Gamma", 1))
elif t == 'student':
wa, wg = ssp.ARStudent(f, order, ssp.parameter("DF", 1))
lap(t)
ws = ssp.ARSpectrum(wa, wg, nSpec=128)
# ridge = Parameter('Ridge', 0.1)
# a, g = ARRidge(a, lpOrder, ridge)
# a, g = ARLasso(a, lpOrder, ridge)
elif frontend == "snr":
a = ssp.Periodogram(f)
n = ssp.Noise(a)
a = ssp.SNRSpectrum(a, n * 0.1)
a = ssp.Autocorrelation(a, input='psd')
a, g = ssp.ARLevinson(a, lpOrder)
a = ssp.AutocorrelationAllPassWarp(a,
alpha=ssp.mel[pcm.rate],
size=lpOrder + 1)
elif frontend == "sparse":
a, g = ssp.ARSparse(f, lpOrder, ssp.parameter("Gamma", 1.414))
elif frontend == "student":
m = ssp.AllPassWarpMatrix(frameSize, ssp.mel[pcm.rate])
fw = np.dot(f, m.T)
a, g = ssp.ARStudent(fw, lpOrder, ssp.parameter("DoF", 1.0))
else:
print("Unknown front end", frontend)
# a, g = ARAllPassWarp(a, g, alpha=mel[r])
# Finally, turn the AR coefs into cepstrum
a = ssp.ARCepstrum(a, g, ssp.parameter("nCepstra", 12))
m = ssp.Mean(a)
a = ssp.Subtract(a, m)
m = ssp.StdDev(a)
a = ssp.Divide(a, m)
print("htk: ", saveFile)