def runTest(self):
print "------------------ Test3 Populate from MP coeffs ---------"
fileIndex = 2
RandomAudioFilePath = file_names[fileIndex]
print 'Working on %s' % RandomAudioFilePath
sizes = [2**j for j in range(7, 15)]
segDuration = 5
nbAtom = 20
pySig = signals.Signal(op.join(audio_files_path, RandomAudioFilePath),
mono=True,
normalize=True)
segmentLength = ((segDuration * pySig.fs) / sizes[-1]) * sizes[-1]
nbSeg = floor(pySig.length / segmentLength)
# cropping
pySig.crop(0, segmentLength)
# create dictionary
pyDico = Dico(sizes)
approx, decay = mp.mp(pySig, pyDico, 20, nbAtom, pad=True, debug=0)
ppdb = XMDCTBDB('MPdb.db', load=False)
# ppdb.keyformat = None
ppdb.populate(approx, None, fileIndex)
nKeys = ppdb.get_stats()['ndata']
# compare the number of keys in the base to the number of atoms
print ppdb.get_stats()
self.assertEqual(nKeys, approx.atom_number)
# now try to recover the fileIndex knowing one of the atoms
Key = [
log(approx.atoms[0].length, 2),
approx.atoms[0].reduced_frequency * pySig.fs
]
T, fileI = ppdb.get(Key)
Treal = (float(approx.atoms[0].time_position) / float(pySig.fs))
print T, Treal
self.assertEqual(fileI[0], fileIndex)
Tpy = np.array(T)
self.assertTrue((np.abs(Tpy - Treal)).min() < 0.1)
# last check: what does a request for non-existing atom in base return?
T, fileI = ppdb.get((11, 120.0))
self.assertEqual(T, [])
self.assertEqual(fileI, [])
# now let's just retrieve the atoms from the base and see if they are
# the same
histograms = ppdb.retrieve(approx, None, offset=0)
# plt.figure()
# plt.imshow(histograms[0:10,:])
# plt.show()
del ppdb
def runTest(self):
''' take the base previously constructed and retrieve the song index based on 10 atoms
'''
print "------------------ Test6 recognition ---------"
nbCandidates = 8
ppdb = XMDCTBDB('LargeMPdb.db', load=True)
print 'Large Db of ' + str(ppdb.get_stats()['nkeys']) + ' and ' + str(
ppdb.get_stats()['ndata'])
# Now take a song, decompose it and try to retrieve it
fileIndex = 6
RandomAudioFilePath = file_names[fileIndex]
print 'Working on ' + str(RandomAudioFilePath)
pySig = signals.Signal(op.join(audio_files_path, RandomAudioFilePath),
mono=True)
pyDico = LODico(sizes)
segDuration = 5
offsetDuration = 7
offset = offsetDuration * pySig.fs
nbAtom = 50
segmentLength = ((segDuration * pySig.fs) / sizes[-1]) * sizes[-1]
pySig.crop(offset, offset + segmentLength)
approx, decay = mp.mp(pySig, pyDico, 40, nbAtom, pad=True)
# plt.figure()
# approx.plotTF()
# plt.show()
res = map(ppdb.get, map(ppdb.kform, approx.atoms),
[(a.time_position - pyDico.get_pad()) / approx.fs
for a in approx.atoms])
#
#res = map(bdb.get, map(bdb.kform, approx.atoms))
histogram = np.zeros((600, nbCandidates))
for i in range(approx.atom_number):
print res[i]
histogram[res[i]] += 1
max1 = np.argmax(histogram[:])
Offset1 = max1 / nbCandidates
estFile1 = max1 % nbCandidates
# candidates , offsets = ppdb.retrieve(approx);
# print approx.atom_number
histograms = ppdb.retrieve(approx, None, offset=0, nbCandidates=8)
# print histograms , np.max(histograms) , np.argmax(histograms, axis=0) ,
# np.argmax(histograms, axis=1)
# plt.figure()
# plt.imshow(histograms[0:20,:],interpolation='nearest')
# plt.show()
maxI = np.argmax(histograms[:])
OffsetI = maxI / nbCandidates
estFileI = maxI % nbCandidates
print fileIndex, offsetDuration, estFileI, OffsetI, estFile1, Offset1, max1, maxI
import matplotlib.pyplot as plt
# plt.figure(figsize=(12,6))
# plt.subplot(121)
# plt.imshow(histograms,aspect='auto',interpolation='nearest')
# plt.subplot(122)
# plt.imshow(histogram,aspect='auto',interpolation='nearest')
## plt.imshow(histograms,aspect='auto',interpolation='nearest')
## plt.colorbar()
# plt.show()
print maxI, OffsetI, estFileI
self.assertEqual(histograms[OffsetI, estFileI], np.max(histograms))
self.assertEqual(fileIndex, estFileI)
self.assertTrue(abs(offsetDuration - OffsetI) <= 2.5)
def runTest(self):
ppdb = XMDCTBDB('tempdb.db',
load=False,
persistent=True,
time_max=500.0)
pySig = signals.LongSignal(op.join(audio_files_path, file_names[0]),
frame_duration=5,
mono=False,
Noverlap=0)
self.assertEqual(pySig.segment_size, 5.0 * pySig.fs)
max_nb_seg = 10
nb_atoms = 150
scales = SpreadDico([8192], penalty=0.1, mask_time=2, mask_freq=20)
# scales = Dico([8192])
for segIdx in range(min(max_nb_seg, pySig.n_seg)):
pySigLocal = pySig.get_sub_signal(segIdx,
1,
mono=True,
normalize=False,
channel=0,
pad=scales.get_pad())
print "MP on segment %d" % segIdx
# run the decomposition
approx, decay = mp.mp(pySigLocal, scales, 2, nb_atoms, pad=False)
print "Populating database with offset " + str(
segIdx * pySig.segment_size / pySig.fs)
ppdb.populate(approx,
None,
0,
offset=float((segIdx * pySig.segment_size) -
scales.get_pad()) / float(pySig.fs))
# ok we have a DB with only 1 file and different segments, now
nb_test_seg = 15
long_sig_test = signals.LongSignal(op.join(audio_files_path,
file_names[0]),
frame_duration=5,
mono=False,
Noverlap=0.5)
count = 0
for segIdx in range(min(nb_test_seg, long_sig_test.n_seg)):
pySigLocal = long_sig_test.get_sub_signal(segIdx,
1,
mono=True,
normalize=False,
channel=0,
pad=scales.get_pad())
# print "MP on segment %d" % segIdx
# run the decomposition
approx, decay = mp.mp(pySigLocal, scales, 2, nb_atoms, pad=False)
print approx.atom_number
histograms = ppdb.retrieve(approx, None, nbCandidates=1)
maxI = np.argmax(histograms[:])
OffsetI = maxI / 1
estFileI = maxI % 1
oracle_value = segIdx * long_sig_test.segment_size * (
1 - long_sig_test.overlap) / long_sig_test.fs
print "Seg %d Oracle: %1.1f - found %1.1f" % (segIdx, oracle_value,
OffsetI)
if abs(OffsetI - oracle_value) < 5:
count += 1
glob = float(count) / float(min(nb_test_seg, long_sig_test.n_seg))
print "Global Score of %1.3f" % glob
self.assertGreater(glob, 0.8)