diar_res.data_object.label = label
diar_res.data_object.time = time
diar_res.data_object.duration = duration
diar_res.data_object.label_metadata.label = dict()
for lab in diar_res.data_object.label:
diar_res.data_object.label_metadata.label[lab] = str(lab)
self.add_result(diar_res)
# Generate Grapher for IRITSpeech4Hz analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayLimsiDiarization = DisplayAnalyzer.create(
analyzer=LimsiDiarization,
analyzer_parameters={'sad_model': 'etape'},
result_id='limsi_diarization.speakers',
grapher_id='grapher_limsi_diarization_speakers',
grapher_name='Speaker diarization (ETAPE)',
background='waveform',
staging=False)
DisplayLimsiDiarization = DisplayAnalyzer.create(
analyzer=LimsiDiarization,
analyzer_parameters={'sad_model': 'maya'},
result_id='limsi_diarization.speakers',
grapher_id='grapher_limsi_diarization_speakers_maya',
grapher_name='Speaker diarization (Mayan)',
background='waveform',
staging=False)
def post_process(self):
pitch = self.new_result(data_mode='value', time_mode='framewise')
# parameters : None # TODO check with Piem "default" and "freq" in
# setup
pitch.id_metadata.id += '.' + "pitch"
pitch.id_metadata.name += ' ' + "pitch"
pitch.id_metadata.unit = "Hz"
pitch.data_object.value = self.pitches
self.add_result(pitch)
pitch_confidence = self.new_result(
data_mode='value', time_mode='framewise')
pitch_confidence.id_metadata.id += '.' + "pitch_confidence"
pitch_confidence.id_metadata.name += ' ' + "pitch confidence"
pitch_confidence.id_metadata.unit = None
pitch_confidence.data_object.value = self.pitch_confidences
self.add_result(pitch_confidence)
# Generate Grapher for Aubio Pitch analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayAubioPitch = DisplayAnalyzer.create(
analyzer=AubioPitch,
result_id='aubio_pitch.pitch',
grapher_id='grapher_aubio_pitch',
grapher_name='Pitch',
background='spectrogram')
#
for a in range(1, len(debut)):
time = float(fin[a] - debut[a]) / 100
sing_result = self.new_result(data_mode='label', time_mode='segment')
# sing_result.id_metadata.id += '.' + 'segment'
sing_result.data_object.label = label
sing_result.data_object.time = np.asarray(debut) / 100
sing_result.data_object.duration = (np.asarray(fin) -
np.asarray(debut)) / 100
sing_result.data_object.label_metadata.label = {
0: 'No Singing',
1: 'Singing'
}
self.add_result(sing_result)
# Generate Grapher for Labri Singing detection analyzer
from timeside.core.grapher import DisplayAnalyzer
# Labri Singing
DisplayLABRI_SING = DisplayAnalyzer.create(
analyzer=LabriSing,
analyzer_parameters={},
result_id='labri_singing',
grapher_id='grapher_labri_singing',
grapher_name='Singing voice detection',
background='waveform',
staging=False)
dmap[c] = (vmin, distances)
vmin = 10
selected_c = None
for c in dmap:
if dmap[c][0] < vmin:
selected_c = c
vmin = dmap[c][0]
if selected_c is not None and len(dmap[selected_c][1]) > 0:
for d, c in dmap[selected_c][1]:
selected_c.fusion(c)
classes.remove(c)
segments = []
for i, group in enumerate(classes):
for seg in group.segments:
segments += [(seg[0], seg[1], i)]
return segments
from timeside.core.grapher import DisplayAnalyzer
DisplayIritSingingTurns = DisplayAnalyzer.create(
analyzer=IRITDECAP,
result_id='irit_singing_turns',
grapher_id='grapher_irit_singingturns',
grapher_name='Singings turns',
staging=True)
# S[S<1e-3]=0
np.maximum(S, 1e-3, out=S)
# Differentiator filter
df_filter = signal.fir_filter_design.remez(31, [0, 0.5], [Pi],
type='differentiator')
S_diff = signal.lfilter(df_filter, 1, S, axis=0)
S_diff[S_diff < 1e-10] = 0
# Summation along the frequency axis
odf_diff = S_diff.sum(axis=1)
odf_median = np.median(odf_diff)
if odf_median:
odf_diff = odf_diff / odf_median # Normalize
odf = self.new_result(data_mode='value', time_mode='framewise')
#odf.parameters = {'FFT_SIZE': self.FFT_SIZE}
odf.data_object.value = odf_diff
self.add_result(odf)
# Generate Grapher for Onset Detection Function analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayOnsetDetectionFunction = DisplayAnalyzer.create(
analyzer=OnsetDetectionFunction,
result_id='onset_detection_function',
grapher_id='grapher_onset_detection_function',
grapher_name='Onset detection')
vmin = 10
selected_c = None
for c in dmap:
if dmap[c][0] < vmin:
selected_c = c
vmin = dmap[c][0]
if selected_c is not None and len(dmap[selected_c][1]) > 0:
for d, c in dmap[selected_c][1]:
selected_c.fusion(c)
classes.remove(c)
segments = []
for i, group in enumerate(classes):
for seg in group.segments:
segments += [(seg[0], seg[1], i)]
return segments
from timeside.core.grapher import DisplayAnalyzer
DisplayIritSingingTurns = DisplayAnalyzer.create(
analyzer=IRITDECAP,
result_id='irit_singing_turns',
grapher_id='grapher_irit_singingturns',
grapher_name='Singings turns',
staging=True)
# segList = segmentFromValues(medfilt(modEnergyValue > self.threshold, 31))
segs = self.new_result(data_mode='label', time_mode='segment')
segs.id_metadata.id += '.' + 'segments'
segs.id_metadata.name += ' ' + 'Segments'
segs.data_object.label_metadata.label = label
segs.data_object.label = [convert[s[2]] for s in segList]
segs.data_object.time = [tLine[s[0]] for s in segList]
segs.data_object.duration = [tLine[s[1]] - tLine[s[0]]
for s in segList]
self.add_result(segs)
return
def getBoundariesInInterval(start, stop, boundaries):
return [t for t in boundaries if t >= start and t <= stop]
# Generate Grapher for IRITMusicSNB analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayMusicSNB = DisplayAnalyzer.create(
analyzer=IRITMusicSNB,
result_id='irit_music_snb.segments',
grapher_id='grapher_irit_music_snb_segments',
grapher_name='Music Detector - Segment Number',
background='waveform',
staging=True)
l1 = len(v1)
l2 = len(v2)
decal = numpy.abs(m1 - m2)
if m1 >= m2:
fin = numpy.min([l1 - decal, l2])
if fin - decal > min_overlap:
v1_out = numpy.array(v1[decal:decal + fin])
v2_out = numpy.array(v2[:fin])
d = numpy.mean(numpy.abs(v1_out - v2_out))
else:
v1_out = [0]
v2_out = [1]
d = 1
else:
return computeDist(v2, v1, min_overlap)
return d, v1_out, v2_out
# Generate Grapher for IRITStartSeg analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayIRIT_Start = DisplayAnalyzer.create(
analyzer=IRITStartSeg,
result_id='irit_startseg.segments',
grapher_id='grapher_irit_startseg',
grapher_name='Analogous start point',
background='waveform',
staging=False)
def name():
return "Constant Q transform from QMUL vamp plugins"
@staticmethod
@interfacedoc
def unit():
return ""
def post_process(self):
super(VampConstantQ, self).post_process() # get remaining results
constant_q = self.new_result(data_mode='value', time_mode='framewise')
midi_pitches = np.arange(self.minpitch, self.maxpitch, 12.0 / self.bpo)
constant_q.data_object.y_value = [midi2freq(midi_number=p, tuningA4=self.tuning)
for p in midi_pitches]
constant_q.data_object.value = self.vamp_results['matrix'][1]
self.add_result(constant_q)
# Generate Grapher for CQT analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayCQT = DisplayAnalyzer.create(
analyzer=VampConstantQ,
result_id='vamp_constantq',
grapher_id='grapher_vamp_cqt',
grapher_name='Constant Q Transform',
staging=False)
# fin[a]=fin[a+1]
# fin=np.delete(fin,a)
# label=np.delete(label,a)
#
for a in range(1, len(debut)):
time = float(fin[a] - debut[a]) / 100
sing_result = self.new_result(data_mode='label', time_mode='segment')
# sing_result.id_metadata.id += '.' + 'segment'
sing_result.data_object.label = label
sing_result.data_object.time = np.asarray(debut) /100
sing_result.data_object.duration = (np.asarray(fin) - np.asarray(debut)) / 100
sing_result.data_object.label_metadata.label = {0: 'No Singing', 1: 'Singing'}
self.add_result(sing_result)
# Generate Grapher for Labri Singing detection analyzer
from timeside.core.grapher import DisplayAnalyzer
# Labri Singing
DisplayLABRI_SING = DisplayAnalyzer.create(
analyzer=LabriSing,
analyzer_parameters={},
result_id='labri_singing',
grapher_id='grapher_labri_singing',
grapher_name='Labri singing voice detection',
background='waveform',
staging=False)
self.input_samplerate)
for s in segList_filt]
med_segs.data_object.duration = [(np.float(s[1] - s[0] + 1) * self.input_stepsize /
self.input_samplerate)
for s in segList_filt]
self.add_result(med_segs)
return
# Generate Grapher for IRITSpeech4Hz analyzer
from timeside.core.grapher import DisplayAnalyzer
Display4hzSpeechSegmentation = DisplayAnalyzer.create(
analyzer=IRITSpeech4Hz,
result_id='irit_speech_4hz.segments',
grapher_id='grapher_irit_speech_4hz_segments',
grapher_name='Speech activity - 4hz',
background='waveform',
staging=True)
# IRIT 4Hz with median filter
Display4hzSpeechSegmentation = DisplayAnalyzer.create(
analyzer=IRITSpeech4Hz,
result_id='irit_speech_4hz.segments_median',
grapher_id='grapher_irit_speech_4hz_segments_median',
grapher_name='Speech detection (syllabic rate)',
background='waveform',
staging=False)
def process(self, frames, eod=False):
self.values.append(np.abs(np.fft.rfft(frames, self.fft_size)))
return frames, eod
def post_process(self):
spectrogram = self.new_result(data_mode='value', time_mode='framewise')
spectrogram.parameters = {'fft_size': self.fft_size}
# spectrogram.data_object.value = self.values['spectrogram']
spectrogram.data_object.value = self.values
nb_freq = spectrogram.data_object.value.shape[1]
spectrogram.data_object.y_value = (np.arange(0, nb_freq) *
self.samplerate() / self.fft_size)
self.add_result(spectrogram)
# Generate Grapher for Spectrogram analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayLinearSpectrogram = DisplayAnalyzer.create(
analyzer=Spectrogram,
result_id='spectrogram_analyzer',
grapher_id='spectrogram',
grapher_name='Linear Spectrogram',
staging=False)
if __name__ == "__main__":
import doctest
import timeside
doctest.testmod(timeside.plugins.analyzer.spectrogram, verbose=True)
# Output result
gated_loudness = self.new_result(data_mode='value', time_mode='global')
gated_loudness.data_object.value = GatedLoudness
gated_loudness.id_metadata.id += '.gated_loudness'
gated_loudness.id_metadata.name += 'Gated Loudness'
self.add_result(gated_loudness)
relative_threshold = self.new_result(data_mode='value', time_mode='global')
relative_threshold.data_object.value = Gamma_r
relative_threshold.id_metadata.id += '.relative_threshold'
relative_threshold.id_metadata.name += 'Relative Threshold'
self.add_result(relative_threshold)
block_loudness = self.new_result(data_mode='value', time_mode='framewise')
block_loudness.data_object.value = self.l
block_loudness.id_metadata.id += '.block_loudness'
block_loudness.id_metadata.name += ' Block Loudness'
self.add_result(block_loudness)
# Generate Grapher for Loudness ITU
from timeside.core.grapher import DisplayAnalyzer
DisplayLoudnessITU = DisplayAnalyzer.create(
analyzer=LoudnessITU,
result_id='loudness_itu.block_loudness',
grapher_id='grapher_loudness_itu',
grapher_name='Loudness ITU',
staging=False)
segs = self.new_result(data_mode='label', time_mode='segment')
segs.id_metadata.id += '.' + 'segments'
segs.id_metadata.name += ' ' + 'Segments'
segs.data_object.label_metadata.label = label
segs.data_object.label = [convert[s[2]] for s in segList]
segs.data_object.time = [tLine[s[0]] for s in segList]
segs.data_object.duration = [
tLine[s[1]] - tLine[s[0]] for s in segList
]
self.add_result(segs)
return
def getBoundariesInInterval(start, stop, boundaries):
return [t for t in boundaries if t >= start and t <= stop]
# Generate Grapher for IRITMusicSNB analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayMusicSNB = DisplayAnalyzer.create(
analyzer=IRITMusicSNB,
result_id='irit_music_snb.segments',
grapher_id='grapher_irit_music_snb_segments',
grapher_name='Music Detector - Segment Number',
background='waveform',
staging=True)
l1 = len(v1)
l2 = len(v2)
decal = numpy.abs(m1 - m2)
if m1 >= m2:
fin = numpy.min([l1 - decal, l2])
if fin - decal > min_overlap:
v1_out = numpy.array(v1[decal:decal + fin])
v2_out = numpy.array(v2[:fin])
d = numpy.mean(numpy.abs(v1_out - v2_out))
else:
v1_out = [0]
v2_out = [1]
d = 1
else:
return computeDist(v2, v1, min_overlap)
return d, v1_out, v2_out
# Generate Grapher for IRITStartSeg analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayIRIT_Start = DisplayAnalyzer.create(
analyzer=IRITStartSeg,
result_id='irit_startseg.segments',
grapher_id='grapher_irit_startseg',
grapher_name='Analogous start point',
background='waveform',
staging=False)
Boundaries in samples
"""
data = map(abs, data)
boundaries_sample = map(lambda b: int(b*fe), boundaries)
w = w_len*fe
l = len(data)
return zip(boundaries, [sum(data[b:int(min(b+w, l))])-sum(data[int(max([0, b-w])):b]) for b in boundaries_sample])
def get_tempo_spectrum(boundaries, freq_range):
"""
"""
pos, wei = map(array, zip(*boundaries))
j = complex(0, 1)
return map(lambda f: abs(sum(exp(-2.0 * j * pi * f * pos)*wei)), freq_range)
# Generate Grapher for IRITSpeech4Hz analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayIritTempogram = DisplayAnalyzer.create(
analyzer=IRITTempogram,
result_id='irit_tempogram',
grapher_id='grapher_irit_tempogram',
grapher_name='Tempogram - Divergence',
staging=True)
segs = self.new_result(data_mode='label', time_mode='segment')
segs.id_metadata.id += '.' + 'segments'
segs.id_metadata.name += ' ' + 'Segments'
segs.data_object.label_metadata.label = label
segs.data_object.label = [convert[s[2]] for s in segList]
segs.data_object.time = [tLine[s[0]] for s in segList]
segs.data_object.duration = [tLine[s[1]] - tLine[s[0]]
for s in segList]
self.add_result(segs)
return
def getBoundariesInInterval(start, stop, boundaries):
return [t for t in boundaries if t >= start and t <= stop]
# Generate Grapher for IRITMusicSLN analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayMusicSLN = DisplayAnalyzer.create(
analyzer=IRITMusicSLN,
result_id='irit_music_sln.segments',
grapher_id='grapher_irit_music_sln_segments',
grapher_name='Music detection',
background='waveform',
staging=False)
@frames_adapter
def process(self, frames, eod=False):
self.values.append(np.abs(np.fft.rfft(frames, self.fft_size)))
return frames, eod
def post_process(self):
spectrogram = self.new_result(data_mode='value', time_mode='framewise')
spectrogram.parameters = {'fft_size': self.fft_size}
# spectrogram.data_object.value = self.values['spectrogram']
spectrogram.data_object.value = self.values
nb_freq = spectrogram.data_object.value.shape[1]
spectrogram.data_object.y_value = (np.arange(0, nb_freq) *
self.samplerate() / self.fft_size)
self.add_result(spectrogram)
# Generate Grapher for Spectrogram analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayLinearSpectrogram = DisplayAnalyzer.create(
analyzer=Spectrogram,
result_id='spectrogram_analyzer',
grapher_id='spectrogram',
grapher_name='Linear Spectrogram',
staging=False)
if __name__ == "__main__":
import doctest
import timeside
doctest.testmod(timeside.plugins.analyzer.spectrogram, verbose=True)
gated_loudness = self.new_result(data_mode='value', time_mode='global')
gated_loudness.data_object.value = GatedLoudness
gated_loudness.id_metadata.id += '.gated_loudness'
gated_loudness.id_metadata.name += 'Gated Loudness'
self.add_result(gated_loudness)
relative_threshold = self.new_result(data_mode='value',
time_mode='global')
relative_threshold.data_object.value = Gamma_r
relative_threshold.id_metadata.id += '.relative_threshold'
relative_threshold.id_metadata.name += 'Relative Threshold'
self.add_result(relative_threshold)
block_loudness = self.new_result(data_mode='value',
time_mode='framewise')
block_loudness.data_object.value = self.l
block_loudness.id_metadata.id += '.block_loudness'
block_loudness.id_metadata.name += ' Block Loudness'
self.add_result(block_loudness)
# Generate Grapher for Loudness ITU
from timeside.core.grapher import DisplayAnalyzer
DisplayLoudnessITU = DisplayAnalyzer.create(
analyzer=LoudnessITU,
result_id='loudness_itu.block_loudness',
grapher_id='grapher_loudness_itu',
grapher_name='Loudness ITU',
staging=False)
return ""
@downmix_to_mono
@frames_adapter
def process(self, frames, eod=False):
self.silence.append(silence_detection(frames, self.threshold))
return frames, eod
def post_process(self):
silence = self.new_result(data_mode='label', time_mode='segment')
silence.data_object.time = (np.arange(0, len(self.silence) * self.input_stepsize,
self.input_stepsize) / self.input_samplerate)
silence.data_object.label = np.array(self.silence, dtype=int)
duration = self.input_blocksize / float(self.input_samplerate)
silence.data_object.duration = np.ones(silence.data_object.label.shape) * duration
silence.data_object.label_metadata.label = {0: 'Silence', 1: 'Not Silence'}
silence.data_object.merge_segment()
self.add_result(silence)
# Generate Grapher
from timeside.core.grapher import DisplayAnalyzer
DisplayAubioSilence = DisplayAnalyzer.create(
analyzer=AubioSilence,
result_id='aubio_silence',
grapher_id='grapher_aubio_silence',
grapher_name='Aubio Silence',
staging=False)
@staticmethod
@interfacedoc
def name():
return "Waveform Analyzer"
@staticmethod
@interfacedoc
def unit():
return ""
# @downmix_to_mono
# @frames_adapter
def process(self, frames, eod=False):
self.values.append(frames)
return frames, eod
def post_process(self):
waveform = self.new_result(data_mode='value', time_mode='framewise')
waveform.data_object.value = np.vstack(self.values)
self.add_result(waveform)
# Generate Grapher for Waveform analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayWaveform = DisplayAnalyzer.create(analyzer=Waveform,
result_id='waveform_analyzer',
grapher_id='grapher_waveform',
grapher_name='Waveform from Analyzer',
staging=True)
music_result.id_metadata.name = "Labri Music detection"
music_result.data_object.label = music
music_result.data_object.time = np.asarray(start_music) / 100
music_result.data_object.duration = (np.asarray(end_music) - np.asarray(start_music)) / 100
music_result.data_object.label_metadata.label = {0: 'No Music', 1: 'Music'}
self.add_result(music_result)
# Generate Grapher for Labri Speech/Music/Noise detection
from timeside.core.grapher import DisplayAnalyzer
# Labri Speech/Music/Noise --> Speech
DisplayLABRI_PMB = DisplayAnalyzer.create(
analyzer=LabriSMN,
analyzer_parameters={},
result_id='labri_speech_music_noise.speech',
grapher_id='grapher_labri_smn_speech',
grapher_name='Labri Speech Detection',
background='waveform',
staging=True)
# Labri Speech/Music/Noise --> Music
DisplayLABRI_PMB = DisplayAnalyzer.create(
analyzer=LabriSMN,
analyzer_parameters={},
result_id='labri_speech_music_noise.music',
grapher_id='grapher_labri_smn_music',
grapher_name='Labri Music Detection',
background='waveform',
staging=True)
@staticmethod
@interfacedoc
def unit():
return ""
@downmix_to_mono
@frames_adapter
def process(self, frames, eod=False):
if not eod:
w_frame = self.windower(essentia.array(frames.squeeze()))
spectrum = self.spec_alg(w_frame)
spec, mags = self.spec_peaks_alg(spectrum)
self.dissonance.append(self.dissonance_alg(spec, mags))
return frames, eod
def post_process(self):
dissonance = self.new_result(data_mode='value', time_mode='framewise')
dissonance.data_object.value = self.dissonance
self.add_result(dissonance)
# Generate Grapher for Essentia dissonance
from timeside.core.grapher import DisplayAnalyzer
DisplayDissonance = DisplayAnalyzer.create(analyzer=Essentia_Dissonance,
result_id='essentia_dissonance',
grapher_id='grapher_dissonance',
grapher_name='Dissonance',
staging=False)
@downmix_to_mono
@frames_adapter
def process(self, frames, eod=False):
self.silence.append(silence_detection(frames, self.threshold))
return frames, eod
def post_process(self):
silence = self.new_result(data_mode='label', time_mode='segment')
silence.data_object.time = (np.arange(0, len(self.silence) * self.input_stepsize,
self.input_stepsize) / self.input_samplerate)
silence.data_object.label = np.array(self.silence, dtype=int)
duration = self.input_blocksize / float(self.input_samplerate)
silence.data_object.duration = np.ones(silence.data_object.label.shape) * duration
silence.data_object.label_metadata.label = {0: 'Silence', 1: 'Not Silence'}
silence.data_object.merge_segment()
self.add_result(silence)
# Generate Grapher
from timeside.core.grapher import DisplayAnalyzer
DisplayAubioSilence = DisplayAnalyzer.create(
analyzer=AubioSilence,
result_id='aubio_silence',
grapher_id='grapher_aubio_silence',
grapher_name='Aubio Silence',
grapher_version='1.0',
staging=False)
:param beta2:
:param delta:
:return:
"""
m = array(m)
v = array(v)
c0 = log(beta1*beta2/(theta1*theta2))
a1 = m/theta1
b1 = a1**(beta1/delta)
c1 = log(a1)
a2 = v/theta2
b2 = a2**(beta2/delta)
c2 = log(a2)
somme1 = (b1+b2)**delta
pxy = c0+(beta1/delta-1)*c1+(beta2/delta-1)*c2+(delta-2)*log(b1+b2)+log(somme1+1/delta-1)-somme1
return mean(pxy)
# Generate Grapher for IRITMonopoly analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayMonopoly = DisplayAnalyzer.create(
analyzer=IRITMonopoly,
result_id='irit_monopoly.segments',
grapher_id='grapher_irit_monopoly_segments',
grapher_name='Monody/polyphony detection',
background='waveform',
staging=False)
:param delta:
:return:
"""
m = array(m)
v = array(v)
c0 = log(beta1 * beta2 / (theta1 * theta2))
a1 = m / theta1
b1 = a1**(beta1 / delta)
c1 = log(a1)
a2 = v / theta2
b2 = a2**(beta2 / delta)
c2 = log(a2)
somme1 = (b1 + b2)**delta
pxy = c0 + (beta1 / delta - 1) * c1 + (beta2 / delta - 1) * c2 + (
delta - 2) * log(b1 + b2) + log(somme1 + 1 / delta - 1) - somme1
return mean(pxy)
# Generate Grapher for IRITMonopoly analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayMonopoly = DisplayAnalyzer.create(
analyzer=IRITMonopoly,
result_id='irit_monopoly.segments',
grapher_id='grapher_irit_monopoly_segments',
grapher_name='Monody/polyphony detection',
background='waveform',
staging=False)
diar_res.data_object.label = label
diar_res.data_object.time = time
diar_res.data_object.duration = duration
diar_res.data_object.label_metadata.label = dict()
for lab in diar_res.data_object.label:
diar_res.data_object.label_metadata.label[lab] = str(lab)
self.add_result(diar_res)
# Generate Grapher for IRITSpeech4Hz analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayLimsiDiarization = DisplayAnalyzer.create(
analyzer=LimsiDiarization,
analyzer_parameters={'sad_model': 'etape'},
result_id='limsi_diarization.speakers',
grapher_id='grapher_limsi_diarization_speakers',
grapher_name='Speaker diarization (ETAPE)',
background='waveform',
staging=False)
DisplayLimsiDiarization = DisplayAnalyzer.create(
analyzer=LimsiDiarization,
analyzer_parameters={'sad_model': 'maya'},
result_id='limsi_diarization.speakers',
grapher_id='grapher_limsi_diarization_speakers_maya',
grapher_name='Speaker diarization (Mayan)',
background='waveform',
staging=False)
# S[S<1e-3]=0
np.maximum(S, 1e-3, out=S)
# Differentiator filter
df_filter = signal.fir_filter_design.remez(31, [0, 0.5], [Pi],
type='differentiator')
S_diff = signal.lfilter(df_filter, 1, S, axis=0)
S_diff[S_diff < 1e-10] = 0
# Summation along the frequency axis
odf_diff = S_diff.sum(axis=1)
odf_median = np.median(odf_diff)
if odf_median:
odf_diff = odf_diff / odf_median # Normalize
odf = self.new_result(data_mode='value', time_mode='framewise')
#odf.parameters = {'FFT_SIZE': self.FFT_SIZE}
odf.data_object.value = odf_diff
self.add_result(odf)
# Generate Grapher for Onset Detection Function analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayOnsetDetectionFunction = DisplayAnalyzer.create(
analyzer=OnsetDetectionFunction,
result_id='onset_detection_function',
grapher_id='grapher_onset_detection_function',
grapher_name='Onset detection')
sad_seg_result.data_object.label_metadata.label = {
0: 'Not Speech',
1: 'Speech'
}
self.add_result(sad_seg_result)
# Generate Grapher for Limsi SAD analyzer
from timeside.core.grapher import DisplayAnalyzer
# Etape Model
DisplayLIMSI_SAD_etape = DisplayAnalyzer.create(
analyzer=LimsiSad,
analyzer_parameters={'sad_model': 'etape'},
result_id='limsi_sad.sad_segments',
grapher_id='grapher_limsi_sad_etape',
grapher_name='Speech activity - ETAPE',
background='waveform',
staging=True)
# Mayan Model
DisplayLIMSI_SAD_maya = DisplayAnalyzer.create(
analyzer=LimsiSad,
analyzer_parameters={'sad_model': 'maya'},
result_id='limsi_sad.sad_segments',
grapher_id='grapher_limsi_sad_maya',
grapher_name='Speech activity - Mayan',
background='waveform',
staging=True)
# Adaptive Model
sad_seg_result.data_object.label = labels
sad_seg_result.data_object.time = times
sad_seg_result.data_object.duration = durations
sad_seg_result.data_object.label_metadata.label = {0: 'Not Speech', 1: 'Speech'}
self.add_result(sad_seg_result)
# Generate Grapher for Limsi SAD analyzer
from timeside.core.grapher import DisplayAnalyzer
# Etape Model
DisplayLIMSI_SAD_etape = DisplayAnalyzer.create(
analyzer=LimsiSad,
analyzer_parameters={'sad_model': 'etape'},
result_id='limsi_sad.sad_segments',
grapher_id='grapher_limsi_sad_etape',
grapher_name='Speech activity - ETAPE',
background='waveform',
staging=True)
# Mayan Model
DisplayLIMSI_SAD_maya = DisplayAnalyzer.create(
analyzer=LimsiSad,
analyzer_parameters={'sad_model': 'maya'},
result_id='limsi_sad.sad_segments',
grapher_id='grapher_limsi_sad_maya',
grapher_name='Speech activity - Mayan',
background='waveform',
staging=True)
# Adaptive Model
def has_vibrato(serie, sampling_rate, minimum_frequency=4, maximum_frequency=8, Nfft=100):
"""
Calcul de vibrato sur une serie par la méthode de la transformée de Fourier de la dérivée.
"""
vibrato = False
frequency_scale = linspace(0, sampling_rate / 2, Nfft / 2)
index_min_vibrato = argmin(abs(frequency_scale - minimum_frequency))
index_max_vibrato = argmin(abs(frequency_scale - maximum_frequency))
derivative = [v1 - v2 for v1, v2 in zip(serie[:-2], serie[1:])]
fft_derivative = abs(rfft(derivative, Nfft))[:Nfft / 2]
i_max = argmax(fft_derivative)
if index_max_vibrato >= i_max >= index_min_vibrato:
vibrato = True
return vibrato
# Generate Grapher for IRITSinging analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayIritSinging = DisplayAnalyzer.create(
analyzer=IRITSinging,
result_id='irit_singing.segments',
grapher_id='grapher_irit_singing_segments',
grapher_name='Singings detection',
background='waveform',
staging=True)
segs = self.new_result(data_mode='label', time_mode='segment')
segs.id_metadata.id += '.' + 'segments'
segs.id_metadata.name += ' ' + 'Segments'
segs.data_object.label_metadata.label = label
segs.data_object.label = [convert[s[2]] for s in segList]
segs.data_object.time = [(float(s[0]) * self.blocksize() /
self.samplerate())
for s in segList]
segs.data_object.duration = [(float(s[1] - s[0] + 1) * self.blocksize() /
self.samplerate())
for s in segList]
self.add_result(segs)
return
# Generate Grapher for IRITSpeechEntropy analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayEntropySpeechSegmentation = DisplayAnalyzer.create(
analyzer=IRITSpeechEntropy,
result_id='irit_speech_entropy.segments',
grapher_id='grapher_irit_speech_entropy_segments',
grapher_name='Speech detection (signal diversity)',
background='waveform',
staging=False)
return "Constant Q transform from QMUL vamp plugins"
@staticmethod
@interfacedoc
def unit():
return ""
def post_process(self):
super(VampConstantQ, self).post_process() # get remaining results
constant_q = self.new_result(data_mode='value', time_mode='framewise')
midi_pitches = np.arange(self.minpitch, self.maxpitch, 12.0 / self.bpo)
constant_q.data_object.y_value = [
midi2freq(midi_number=p, tuningA4=self.tuning)
for p in midi_pitches
]
constant_q.data_object.value = self.vamp_results['matrix'][1]
self.add_result(constant_q)
# Generate Grapher for CQT analyzer
from timeside.core.grapher import DisplayAnalyzer
DisplayCQT = DisplayAnalyzer.create(analyzer=VampConstantQ,
result_id='vamp_constantq',
grapher_id='grapher_vamp_cqt',
grapher_name='Constant Q Transform',
staging=False)
]
med_segs.data_object.duration = [
(np.float(s[1] - s[0] + 1) * self.input_stepsize /
self.input_samplerate) for s in segList_filt
]
self.add_result(med_segs)
return
# Generate Grapher for IRITSpeech4Hz analyzer
from timeside.core.grapher import DisplayAnalyzer
Display4hzSpeechSegmentation = DisplayAnalyzer.create(
analyzer=IRITSpeech4Hz,
result_id='irit_speech_4hz.segments',
grapher_id='grapher_irit_speech_4hz_segments',
grapher_name='Speech activity - 4hz',
background='waveform',
staging=True)
# IRIT 4Hz with median filter
Display4hzSpeechSegmentation = DisplayAnalyzer.create(
analyzer=IRITSpeech4Hz,
result_id='irit_speech_4hz.segments_median',
grapher_id='grapher_irit_speech_4hz_segments_median',
grapher_name='Speech detection (syllabic rate)',
background='waveform',
staging=False)
