def joint_coding_distortion(target_sig , ref_app, max_rate,
search_width , threshold=0 , doSubtract=True,
debug=0 , discard = False,
precut=-1, initfftw=True):
""" compute the joint coding distortion e.g. given a fixed maximum rate,
what is the distorsion achieved if coding the target_sig knowing the ref_app
This is limited to a time adaptation of the atoms
"""
tolerances = [2]*len(ref_app.dico.sizes)
# initialize the fftw
if initfftw:
mp._initialize_fftw(ref_app.dico, max_thread_num=1)
# initialize factored approx
factorizedApprox = approx.Approx(ref_app.dico,
[],
target_sig,
ref_app.length,
ref_app.fs,
fast_create=True)
timeShifts = np.zeros(ref_app.atom_number)
atom_idx = 0
rate = 0
residual = target_sig.data.copy()
if debug > 0:
print "starting factorization of " , ref_app.atom_number , " atoms"
while (rate < max_rate) and (atom_idx < ref_app.atom_number):
# Stop only when the target rate is achieved or all atoms have been used
atom = ref_app[atom_idx]
# make a copy of the atom
newAtom = atom.copy()
HalfWidth = (tolerances[ref_app.dico.sizes.index(atom.length)] -1) * atom.length/2;
# Search for best time shift using cross correlation
input1 = residual[atom.time_position - HalfWidth : atom.time_position + atom.length + HalfWidth]
input2 = np.zeros((2*HalfWidth + atom.length))
input2[HalfWidth:-HalfWidth] = atom.waveform
if not (input1.shape == input2.shape):
raise ValueError("This certainly happens because you haven't sufficiently padded your signal")
scoreVec = np.array([0.0]);
newts = project_atom(input1,input2 , scoreVec , atom.length)
score = scoreVec
if debug>0:
print "Score of " , score , " found"
# handle MP atoms #
if newAtom.time_shift is not None:
newAtom.time_shift += newts
newAtom.time_position += newts
else:
newAtom.time_shift = newts
newAtom.time_position += newts
atom.proj_score = atom.mdct_value
if debug>0:
print "Factorizing with new offset: " , newts
if score <0:
newAtom.waveform = -newAtom.waveform
factorizedApprox.add(newAtom)
if debug > 0:
print "SRR Achieved of : " , factorizedApprox.compute_srr()
timeShifts[atom_idx] = newAtom.time_shift
if doSubtract:
residual[newAtom.time_position : newAtom.time_position + newAtom.length ] -= newAtom.waveform
rate += np.log2(abs(newts))+1
if debug:
print "Atom %d - rate of %1.3f"%(atom_idx, rate)
atom_idx +=1
# Use to prune the calculus
if atom_idx>precut and precut>0:
curdisto = factorizedApprox.compute_srr()
if curdisto<0:
# pruning
return curdisto
else:
# stop wasting time afterwards
precut = -1
# cleaning
if initfftw:
mp._clean_fftw()
# calculate achieved SNR :
return factorizedApprox.compute_srr()
# Now load the long version
from PyMP.signals import LongSignal
seg_size = 5*8192
long_signal = LongSignal(op.join(os.environ['PYMP_PATH'],'data/Bach_prelude_40s.wav'),
seg_size,
mono=True, Noverlap=0.5)
# decomposing the long signal
apps, decays = mp.mp_long(long_signal,
dico,
target_srr, max_atom_num)
dists = np.zeros((long_signal.n_seg, len(apps)))
mp._initialize_fftw(apps[0].dico, max_thread_num=1)
for idx in range(long_signal.n_seg):
for jdx in range(idx):
# test all preceeding segments only
target_sig = long_signal.get_sub_signal(idx, 1, mono=True, pad=dico.get_pad(),fast_create=True)
dists[idx,jdx] = joint_coding_distortion(target_sig, apps[jdx],max_rate,1024, initfftw=False)
mp._clean_fftw()
plt.figure()
plt.imshow(dists)
plt.colorbar()
plt.show()