def runTest(self):
''' time to test the fingerprinting scheme, create a base with 200 atoms for 8 songs, then
Construct the histograms and retrieve the fileIndex and time offset that is the most
plausible '''
print "------------------ Test5 DB construction ---------"
# # create the base : persistent
ppdb = STFTPeaksBDB('LargeSTFTPeaksdb.db', load=False, time_res=0.2)
print ppdb
segDuration = 5
sig = LongSignal(op.join(audio_files_path, file_names[0]),
frame_duration=segDuration,
mono=False,
Noverlap=0)
segmentLength = sig.segment_size
max_seg_num = 5
# " run sketchifier on a number of files"
nbFiles = 8
keycount = 0
for fileIndex in range(nbFiles):
RandomAudioFilePath = file_names[fileIndex]
print fileIndex, RandomAudioFilePath
if not (RandomAudioFilePath[-3:] == 'wav'):
continue
pySig = LongSignal(op.join(audio_files_path, RandomAudioFilePath),
frame_size=segmentLength,
mono=False,
Noverlap=0)
sk = sketch.STFTPeaksSketch(**{'scale': 512, 'step': 128})
nbSeg = int(pySig.n_seg)
print 'Working on ' + str(RandomAudioFilePath) + ' with ' + str(
nbSeg) + ' segments'
for segIdx in range(min(nbSeg, max_seg_num)):
pySigLocal = pySig.get_sub_signal(segIdx,
1,
True,
True,
channel=0,
pad=0)
print "sketchify the segment %d" % segIdx
# run the decomposition
sk.recompute(pySigLocal)
sk.sparsify(200)
fgpt = sk.fgpt()
print "Populating database with offset " + str(
segIdx * segmentLength / sig.fs)
ppdb.populate(fgpt, sk.params, fileIndex)
# keycount += approx.atom_number
print ppdb.get_stats()
def runTest(self):
ppdb = STFTPeaksBDB('tempdb.db',
load=False,
persistent=True,
time_max=500.0)
pySig = 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 = 400
sk = sketch.STFTPeaksSketch(**{'scale': 512, 'step': 128})
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=0)
print "sketchify segment %d" % segIdx
# run the decomposition
sk.recompute(pySigLocal)
sk.sparsify(nb_atoms)
fgpt = sk.fgpt()
print "Populating database with offset " + str(
segIdx * pySig.segment_size / pySig.fs)
ppdb.populate(fgpt,
sk.params,
0,
offset=segIdx * pySig.segment_size / pySig.fs)
# ok we have a DB with only 1 file and different segments, now
nb_test_seg = 15
long_sig_test = 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=0)
# print "MP on segment %d" % segIdx
# run the decomposition
sk.recompute(pySigLocal)
sk.sparsify(nb_atoms)
fgpt = sk.fgpt()
histograms = ppdb.retrieve(fgpt,
sk.params,
offset=0,
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)
# 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()
