def runTest(self):
# create a SpreadDico
pySig = Signal(op.join(audio_filepath, "glocs.wav"), mono=True)
pySig.crop(0, 5 * pySig.fs)
pySig.pad(2048)
dico = [128, 1024, 8192]
parallelProjections.initialize_plans(np.array(dico), np.array([2] * len(dico)))
classicDIco = mdct_dico.Dico(dico)
spreadDico = mdct_dico.SpreadDico(dico, all_scales=True, penalty=0, maskSize=3)
self.assertEqual(spreadDico.mask_times, [3, 3, 3])
classicDIco.initialize(pySig)
spreadDico.initialize(pySig)
classicDIco.update(pySig, 2)
spreadDico.update(pySig, 2)
classicAtom1 = classicDIco.get_best_atom(0)
spreadAtom1 = spreadDico.get_best_atom(0)
# print classicAtom1, spreadAtom1
self.assertEqual(classicAtom1, spreadAtom1)
pySig.subtract(classicAtom1)
classicDIco.update(pySig, 2)
spreadDico.update(pySig, 2)
classicAtom2 = classicDIco.get_best_atom(0)
spreadAtom2 = spreadDico.get_best_atom(0)
self.assertNotEqual(classicAtom2, spreadAtom2)
def runTest(self):
name = "orchestra"
pySig = Signal(op.join(audio_filepath, "Bach_prelude_40s.wav"), mono=True, normalize=True)
pySig.crop(0, 5 * pySig.fs)
pySig.pad(16384)
sigEnergy = np.sum(pySig.data ** 2)
dico = [128, 1024, 8192]
nbAtoms = 200
classicDIco = mdct_dico.Dico(dico)
spreadDico = mdct_dico.SpreadDico(dico, all_scales=True, penalty=0.1, maskSize=10)
approxClassic, decayClassic = mp.mp(pySig, classicDIco, 20, nbAtoms)
approxSpread, decaySpread = mp.mp(pySig, spreadDico, 20, nbAtoms, pad=False)
import matplotlib.pyplot as plt
plt.figure(figsize=(16, 8))
plt.subplot(121)
approxClassic.plot_tf(ylim=[0, 4000])
plt.title("Classic decomposition : 200 atoms 3xMDCT")
plt.subplot(122)
approxSpread.plot_tf(ylim=[0, 4000])
plt.title("Decomposition with TF masking: 200 atoms 3xMDCT")
# plt.savefig(name + '_TestTFMasking.eps')
plt.figure()
plt.plot([10 * np.log10(i / sigEnergy) for i in decayClassic])
plt.plot([10 * np.log10(i / sigEnergy) for i in decaySpread], "r")
plt.legend(("Classic decomposition", "Spreading Atoms"))
plt.ylabel("Residual energy decay(dB)")
plt.xlabel("Iteration")
def runTest(self):
pySig = Signal(op.join(audio_filepath, "glocs.wav"), mono=True)
pySig.crop(0, 5 * pySig.fs)
pySig.pad(2048)
scale = 1024
parallelProjections.initialize_plans(np.array([scale]), np.array([2]))
classicBlock = mdct_block.Block(scale, pySig, 0, debug_level=3)
spreadBlock = mdct_block.SpreadBlock(scale, pySig, 0, debug_level=3, penalty=0, maskSize=5)
# compute the projections, should be equivalent
classicBlock.update(pySig, 0, -1)
spreadBlock.update(pySig, 0, -1)
maxClassicAtom1 = classicBlock.get_max_atom()
print maxClassicAtom1.length, maxClassicAtom1.frame,
print maxClassicAtom1.freq_bin, maxClassicAtom1.mdct_value
maxSpreadcAtom1 = spreadBlock.get_max_atom()
print maxSpreadcAtom1.length, maxSpreadcAtom1.frame,
print maxSpreadcAtom1.freq_bin, maxSpreadcAtom1.mdct_value
# assert equality using the inner comparison method of MDCT atoms
self.assertEqual(maxClassicAtom1, maxSpreadcAtom1)
# verifying the masking index construction
mask_frame_width = 2
mask_bin_width = 1
spreadBlock.compute_mask(maxSpreadcAtom1, mask_bin_width, mask_frame_width, 0.5)
c_frame = int(np.ceil(maxSpreadcAtom1.time_position / (scale / 2)))
c_bin = int(maxSpreadcAtom1.reduced_frequency * scale)
z1 = np.arange(int(c_frame - mask_frame_width), int(c_frame + mask_frame_width) + 1)
z2 = np.arange(int(c_bin - mask_bin_width), int(c_bin + mask_bin_width) + 1)
# x, y = np.meshgrid(z1, z2)
# print spreadBlock.mask_index_x
# np.testing.assert_array_equal(spreadBlock.mask_index_x, z1)
# np.testing.assert_array_equal(spreadBlock.mask_index_y, z2)
pySig.subtract(maxSpreadcAtom1)
# recompute the projections
classicBlock.update(pySig, 0, -1)
spreadBlock.update(pySig, 0, -1)
# plt.show()
maxClassicAtom2 = classicBlock.get_max_atom()
print maxClassicAtom2.length, maxClassicAtom2.frame, maxClassicAtom2.freq_bin, maxClassicAtom2.mdct_value
maxSpreadcAtom2 = spreadBlock.get_max_atom()
print maxSpreadcAtom2.length, maxSpreadcAtom2.frame, maxSpreadcAtom2.freq_bin, maxSpreadcAtom2.mdct_value
self.assertNotEqual(maxClassicAtom2, maxSpreadcAtom2)
parallelProjections.clean_plans()
def runTest(self):
name = "orchestra"
pySig = Signal(op.join(audio_filepath, "glocs.wav"), mono=True, normalize=True)
pySig.crop(0, 5 * pySig.fs)
pySig.pad(16384)
sigEnergy = np.sum(pySig.data ** 2)
dico = [128, 1024, 8192]
nbAtoms = 200
classicDIco = mdct_dico.Dico(dico, useC=False)
spreadDico = mdct_dico.SpreadDico(
dico, all_scales=False, spread_scales=[1024, 8192], penalty=0.1, mask_time=2, mask_freq=2
)
approxClassic, decayClassic = mp.mp(pySig, classicDIco, 20, nbAtoms)
approxSpread, decaySpread = mp.mp(pySig, spreadDico, 20, nbAtoms, pad=False)
plt.figure(figsize=(16, 8))
plt.subplot(121)
approxClassic.plot_tf(ylim=[0, 4000])
plt.title("Classic decomposition : 200 atoms 3xMDCT")
plt.subplot(122)
approxSpread.plot_tf(ylim=[0, 4000])
plt.title("Decomposition with TF masking: 200 atoms 3xMDCT")
# plt.savefig(name + '_TestTFMasking.eps')
plt.figure()
plt.plot([10 * np.log10(i / sigEnergy) for i in decayClassic])
plt.plot([10 * np.log10(i / sigEnergy) for i in decaySpread], "r")
plt.legend(("Classic decomposition", "Spreading Atoms"))
plt.ylabel("Residual energy decay(dB)")
plt.xlabel("Iteration")
# plt.savefig(name + '_decayTFMasking.eps')
plt.figure()
for blockI in range(1, 3):
block = spreadDico.blocks[blockI]
plt.subplot(2, 2, blockI)
print block.mask.shape, block.mask.shape[0] / (block.scale / 2), block.scale / 2
plt.imshow(
np.reshape(block.mask, (block.mask.shape[0] / (block.scale / 2), block.scale / 2)),
interpolation="nearest",
aspect="auto",
)
plt.colorbar()
plt.subplot(2, 2, blockI + 2)
# print block.mask.shape, block.mask.shape[0] / (block.scale/2),
# block.scale/2
block.im_proj_matrix()
plt.colorbar()
#mpl.rcParams['text.usetex'] = True
from PyMP import Signal, mp
from PyMP.mdct import Dico
sizes = [128, 1024, 8192]
n_atoms = 1000
abPath = os.path.abspath('../../data/')
sig = Signal(abPath + '/glocs.wav', mono=True, normalize=True)
# taking only the first musical phrase (3.5 seconds approximately)
sig.crop(0, 3.5 * sig.fs)
sig.pad(8192)
# add some minor noise to avoid null areas
sig.data += 0.0001 * np.random.randn(sig.length)
# create MDCT multiscale dictionary
dico = Dico(sizes)
# run the MP routine
approx, decay = mp.mp(sig, dico, 50, n_atoms)
# plotting the results
timeVec = np.arange(0, float(sig.length)) / sig.fs
plt.figure(figsize=(10, 6))
axOrig = plt.axes([0.05, 0.55, .4, .4])
import numpy as np
from PyMP import Signal, mp
from PyMP.mdct.dico import Dico, LODico
from PyMP.mdct.atom import Atom
print "Running MP, OMP and local versions on synthetic k-sparse"
scales = [16, 64, 256]
dico = Dico(scales)
M = len(scales)
L = 256 * 4
k = 0.2*L
# create a k-sparse signal
sp_vec = np.zeros(M*L,)
from PyMP.tools import mdct
random_indexes = np.arange(M*L)
np.random.shuffle(random_indexes)
random_weights = np.random.randn(M*L)
sp_vec[random_indexes[0:k]] = random_weights[0:k]
sparse_data = np.zeros(L,)
for m in range(M):
sparse_data += mdct.imdct(sp_vec[m*L:(m+1)*L], scales[m])
signal_original = Signal(sparse_data, Fs=8000, mono=True, normalize=False)
signal_original.data += 0.01 * np.random.random(signal_original.length,)
n_atoms = k
signal_original.pad(dico.get_pad())
app_2, dec2 = mp.greedy(signal_original, dico, 100,
n_atoms, debug=0, pad=False, update='locgp')
app_1, dec1 = mp.greedy(signal_original, dico, 100,
n_atoms, debug=0, pad=False, update='mp')
app_3, dec3 = mp.greedy(signal_original, dico, 100,
n_atoms, debug=0, pad=False, update='locomp')
# sig.plot()
# sig.write('newDestFile.wav')
# editing
print 'Before cropping Length of ', sig.length
sig.crop(0, 2048)
print 'After cropping Length of ', sig.length
sub_sig = sig[0:2048]
print sub_sig
new_sig = Signal(np.ones((8,)), 1)
new_sig.data
print "Padding"
new_sig.pad(4)
new_sig.data
print "De-Padding"
new_sig.depad(4)
new_sig.data
dico = Dico([128, 1024, 8192])
sig = Signal('../data/glocs.wav', mono=True)
app = approx.Approx(dico, [], sig)
app.add(atom.Atom(256, 1, 256, 10, 8000, 1))
app.compute_srr()
print app
"""
Tutorial provided as part of PyMP
M. Moussallam
"""
from PyMP.mdct import Dico, LODico
from PyMP.mdct.rand import SequenceDico
from PyMP import mp, mp_coder, Signal
signal = Signal('../data/ClocheB.wav', mono=True) # Load Signal
signal.crop(0, 4.0 * signal.fs) # Keep only 4 seconds
# atom of scales 8, 64 and 512 ms
scales = [(s * signal.fs / 1000) for s in (8, 64, 512)]
signal.pad(scales[-1])
# Dictionary for Standard MP
dico = Dico(scales)
# Launching decomposition, stops either at 20 dB of SRR or 2000 iterations
app, dec = mp.mp(signal, dico, 20, 2000, pad=False)
app.atom_number
snr, bitrate, quantized_app = mp_coder.simple_mdct_encoding(
app, 8000, Q=14)
print (snr, bitrate)
print "With Q=5"
snr, bitrate, quantized_app = mp_coder.simple_mdct_encoding(
app, 8000, Q=5)
print (snr, bitrate)
