def compute(audio, pool, options):
# analysis parameters
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
windowType = options['windowType']
# frame algorithms
frames = essentia.FrameGenerator(audio=audio,
frameSize=frameSize,
hopSize=hopSize)
window = essentia.Windowing(size=frameSize, zeroPadding=0, type=windowType)
spectrum = essentia.Spectrum(size=frameSize)
# spectral algorithms
energy = essentia.Energy()
mfcc = essentia.MFCC(highFrequencyBound=8000)
INFO('Computing Low-Level descriptors necessary for segmentation...')
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize * 0.5
progress = Progress(total=total_frames)
for frame in frames:
frameScope = [
start_of_frame / sampleRate,
(start_of_frame + frameSize) / sampleRate
]
#pool.setCurrentScope(frameScope)
pool.add(namespace + '.' + 'scope', frameScope)
if options['skipSilence'] and essentia.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# need the energy for getting the thumbnail
pool.add(namespace + '.' + 'spectral_energy', energy(frame_spectrum))
# mfcc
(frame_melbands, frame_mfcc) = mfcc(frame_spectrum)
pool.add(namespace + '.' + 'spectral_mfcc', frame_mfcc)
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
progress.finish()
def compute(audio, pool, options):
# analysis parameters
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
windowType = options['windowType']
# frame algorithms
frames = essentia.FrameGenerator(audio = audio, frameSize = frameSize, hopSize = hopSize)
window = essentia.Windowing(size = frameSize, zeroPadding = 0, type = windowType)
spectrum = essentia.Spectrum(size = frameSize)
# spectral algorithms
energy = essentia.Energy()
mfcc = essentia.MFCC(highFrequencyBound = 8000)
INFO('Computing Low-Level descriptors necessary for segmentation...')
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize*0.5
progress = Progress(total = total_frames)
for frame in frames:
frameScope = [ start_of_frame / sampleRate, (start_of_frame + frameSize) / sampleRate ]
#pool.setCurrentScope(frameScope)
pool.add(namespace + '.' + 'scope', frameScope)
if options['skipSilence'] and essentia.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# need the energy for getting the thumbnail
pool.add(namespace + '.' + 'spectral_energy', energy(frame_spectrum))
# mfcc
(frame_melbands, frame_mfcc) = mfcc(frame_spectrum)
pool.add(namespace + '.' + 'spectral_mfcc', frame_mfcc)
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
progress.finish()
def compute(audio, pool, options):
# analysis parameters
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
windowType = options['windowType']
# temporal descriptors
lpc = essentia.LPC(order = 10, type = 'warped', sampleRate = sampleRate)
zerocrossingrate = essentia.ZeroCrossingRate()
# frame algorithms
frames = essentia.FrameGenerator(audio = audio, frameSize = frameSize, hopSize = hopSize)
window = essentia.Windowing(size = frameSize, zeroPadding = 0, type = windowType)
spectrum = essentia.Spectrum(size = frameSize)
# spectral algorithms
barkbands = essentia.BarkBands(sampleRate = sampleRate)
centralmoments = essentia.SpectralCentralMoments()
crest = essentia.Crest()
centroid = essentia.SpectralCentroid()
decrease = essentia.SpectralDecrease()
spectral_contrast = essentia.SpectralContrast(frameSize = frameSize,
sampleRate = sampleRate,
numberBands = 6,
lowFrequencyBound = 20,
highFrequencyBound = 11000,
neighbourRatio = 0.4,
staticDistribution = 0.15)
distributionshape = essentia.DistributionShape()
energy = essentia.Energy()
# energyband_bass, energyband_middle and energyband_high parameters come from "standard" hi-fi equalizers
energyband_bass = essentia.EnergyBand(startCutoffFrequency = 20.0, stopCutoffFrequency = 150.0, sampleRate = sampleRate)
energyband_middle_low = essentia.EnergyBand(startCutoffFrequency = 150.0, stopCutoffFrequency = 800.0, sampleRate = sampleRate)
energyband_middle_high = essentia.EnergyBand(startCutoffFrequency = 800.0, stopCutoffFrequency = 4000.0, sampleRate = sampleRate)
energyband_high = essentia.EnergyBand(startCutoffFrequency = 4000.0, stopCutoffFrequency = 20000.0, sampleRate = sampleRate)
flatnessdb = essentia.FlatnessDB()
flux = essentia.Flux()
harmonic_peaks = essentia.HarmonicPeaks()
hfc = essentia.HFC()
mfcc = essentia.MFCC()
rolloff = essentia.RollOff()
rms = essentia.RMS()
strongpeak = essentia.StrongPeak()
# pitch algorithms
pitch_detection = essentia.PitchDetection(frameSize = frameSize, sampleRate = sampleRate)
pitch_salience = essentia.PitchSalience()
# dissonance
spectral_peaks = essentia.SpectralPeaks(sampleRate = sampleRate, orderBy='frequency')
dissonance = essentia.Dissonance()
# spectral complexity
# magnitudeThreshold = 0.005 is hardcoded for a "blackmanharris62" frame
spectral_complexity = essentia.SpectralComplexity(magnitudeThreshold = 0.005)
INFO('Computing Low-Level descriptors...')
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize*0.5
pitches, pitch_confidences = [],[]
progress = Progress(total = total_frames)
scPool = essentia.Pool() # pool for spectral contrast
for frame in frames:
frameScope = [ start_of_frame / sampleRate, (start_of_frame + frameSize) / sampleRate ]
#pool.setCurrentScope(frameScope)
# silence rate
pool.add(namespace + '.' + 'silence_rate_60dB', essentia.isSilent(frame))
pool.add(namespace + '.' + 'silence_rate_30dB', is_silent_threshold(frame, -30))
pool.add(namespace + '.' + 'silence_rate_20dB', is_silent_threshold(frame, -20))
if options['skipSilence'] and essentia.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
# temporal descriptors
pool.add(namespace + '.' + 'zerocrossingrate', zerocrossingrate(frame))
(frame_lpc, frame_lpc_reflection) = lpc(frame)
pool.add(namespace + '.' + 'temporal_lpc', frame_lpc)
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# spectrum-based descriptors
power_spectrum = frame_spectrum ** 2
pool.add(namespace + '.' + 'spectral_centroid', centroid(power_spectrum))
pool.add(namespace + '.' + 'spectral_decrease', decrease(power_spectrum))
pool.add(namespace + '.' + 'spectral_energy', energy(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_low', energyband_bass(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_middle_low', energyband_middle_low(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_middle_high', energyband_middle_high(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_high', energyband_high(frame_spectrum))
pool.add(namespace + '.' + 'hfc', hfc(frame_spectrum))
pool.add(namespace + '.' + 'spectral_rms', rms(frame_spectrum))
pool.add(namespace + '.' + 'spectral_flux', flux(frame_spectrum))
pool.add(namespace + '.' + 'spectral_rolloff', rolloff(frame_spectrum))
pool.add(namespace + '.' + 'spectral_strongpeak', strongpeak(frame_spectrum))
# central moments descriptors
frame_centralmoments = centralmoments(power_spectrum)
(frame_spread, frame_skewness, frame_kurtosis) = distributionshape(frame_centralmoments)
pool.add(namespace + '.' + 'spectral_kurtosis', frame_kurtosis)
pool.add(namespace + '.' + 'spectral_spread', frame_spread)
pool.add(namespace + '.' + 'spectral_skewness', frame_skewness)
# dissonance
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
frame_dissonance = dissonance(frame_frequencies, frame_magnitudes)
pool.add(namespace + '.' + 'dissonance', frame_dissonance)
# mfcc
(frame_melbands, frame_mfcc) = mfcc(frame_spectrum)
pool.add(namespace + '.' + 'mfcc', frame_mfcc)
# spectral contrast
(sc_coeffs, sc_valleys) = spectral_contrast(frame_spectrum)
scPool.add(namespace + '.' + 'sccoeffs', sc_coeffs)
scPool.add(namespace + '.' + 'scvalleys', sc_valleys)
# barkbands-based descriptors
frame_barkbands = barkbands(frame_spectrum)
pool.add(namespace + '.' + 'barkbands', frame_barkbands)
pool.add(namespace + '.' + 'spectral_crest', crest(frame_barkbands))
pool.add(namespace + '.' + 'spectral_flatness_db', flatnessdb(frame_barkbands))
barkbands_centralmoments = essentia.CentralMoments(range = len(frame_barkbands) - 1)
(barkbands_spread, barkbands_skewness, barkbands_kurtosis) = distributionshape(barkbands_centralmoments(frame_barkbands))
pool.add(namespace + '.' + 'barkbands_spread', barkbands_spread)
pool.add(namespace + '.' + 'barkbands_skewness', barkbands_skewness)
pool.add(namespace + '.' + 'barkbands_kurtosis', barkbands_kurtosis)
# pitch descriptors
frame_pitch, frame_pitch_confidence = pitch_detection(frame_spectrum)
if frame_pitch > 0 and frame_pitch <= 20000.:
pool.add(namespace + '.' + 'pitch', frame_pitch)
pitches.append(frame_pitch)
pitch_confidences.append(frame_pitch_confidence)
pool.add(namespace + '.' + 'pitch_instantaneous_confidence', frame_pitch_confidence)
frame_pitch_salience = pitch_salience(frame_spectrum[:-1])
pool.add(namespace + '.' + 'pitch_salience', frame_pitch_salience)
# spectral complexity
pool.add(namespace + '.' + 'spectral_complexity', spectral_complexity(frame_spectrum))
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
# if no 'temporal_zerocrossingrate' it means that this is a silent file
if 'zerocrossingrate' not in descriptorNames(pool.descriptorNames(), namespace):
raise essentia.EssentiaError('This is a silent file!')
spectralContrastPCA(scPool, pool)
# build pitch value histogram
from math import log
from numpy import bincount
# convert from Hz to midi notes
midipitches = []
unknown = 0
for freq in pitches:
if freq > 0. and freq <= 12600:
midipitches.append(12*(log(freq/6.875)/0.69314718055995)-3.)
else:
unknown += 1
if len(midipitches) > 0:
# compute histogram
midipitchhist = bincount(midipitches)
# set 0 midi pitch to be the number of pruned value
midipitchhist[0] = unknown
# normalise
midipitchhist = [val/float(sum(midipitchhist)) for val in midipitchhist]
# zero pad
for i in range(128 - len(midipitchhist)): midipitchhist.append(0.0)
else:
midipitchhist = [0.]*128
midipitchhist[0] = 1.
# pitchhist = essentia.array(zip(range(len(midipitchhist)), midipitchhist))
pool.add(namespace + '.' + 'spectral_pitch_histogram', midipitchhist)#, pool.GlobalScope)
# the code below is the same as the one above:
#for note in midipitchhist:
# pool.add(namespace + '.' + 'spectral_pitch_histogram_values', note)
# print "midi note:", note
pitch_centralmoments = essentia.CentralMoments(range = len(midipitchhist) - 1)
(pitch_histogram_spread, pitch_histogram_skewness, pitch_histogram_kurtosis) = distributionshape(pitch_centralmoments(midipitchhist))
pool.add(namespace + '.' + 'spectral_pitch_histogram_spread', pitch_histogram_spread)#, pool.GlobalScope)
progress.finish()
def compute(audio, pool, options):
INFO('Computing SFX descriptors...')
# analysis parameters
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
windowType = options['windowType']
# frame algorithms
frames = essentia.FrameGenerator(audio = audio, frameSize = frameSize, hopSize = hopSize)
window = essentia.Windowing(size = frameSize, zeroPadding = 0, type = windowType)
spectrum = essentia.Spectrum(size = frameSize)
# pitch algorithm
pitch_detection = essentia.PitchDetection(frameSize = 2048, sampleRate = sampleRate)
# sfx descriptors
spectral_peaks = essentia.SpectralPeaks(sampleRate = sampleRate, orderBy = 'frequency')
harmonic_peaks = essentia.HarmonicPeaks()
inharmonicity = essentia.Inharmonicity()
odd2evenharmonicenergyratio = essentia.OddToEvenHarmonicEnergyRatio()
tristimulus = essentia.Tristimulus()
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize*0.5
progress = Progress(total = total_frames)
for frame in frames:
frameScope = [ start_of_frame / sampleRate, (start_of_frame + frameSize) / sampleRate ]
#pool.setCurrentScope(frameScope)
if options['skipSilence'] and essentia.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# pitch descriptors
frame_pitch, frame_pitch_confidence = pitch_detection(frame_spectrum)
# spectral peaks based descriptors
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
(frame_harmonic_frequencies, frame_harmonic_magnitudes) = harmonic_peaks(frame_frequencies, frame_magnitudes, frame_pitch)
if len(frame_harmonic_frequencies) > 1:
frame_inharmonicity = inharmonicity(frame_harmonic_frequencies, frame_harmonic_magnitudes)
pool.add(namespace + '.' + 'inharmonicity', frame_inharmonicity)
frame_tristimulus = tristimulus(frame_harmonic_frequencies, frame_harmonic_magnitudes)
pool.add(namespace + '.' + 'tristimulus', frame_tristimulus)
frame_odd2evenharmonicenergyratio = odd2evenharmonicenergyratio(frame_harmonic_frequencies, frame_harmonic_magnitudes)
pool.add(namespace + '.' + 'odd2evenharmonicenergyratio', frame_odd2evenharmonicenergyratio)
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
envelope = essentia.Envelope()
file_envelope = envelope(audio)
# temporal statistics
decrease = essentia.AudioDecrease(blockSize = len(audio))
pool.add(namespace + '.' + 'temporal_decrease', decrease(file_envelope))#, pool.GlobalScope)
centralmoments = essentia.AudioCentralMoments(blockSize = len(audio))
file_centralmoments = centralmoments(file_envelope)
distributionshape = essentia.DistributionShape()
(file_spread, file_skewness, file_kurtosis) = distributionshape(file_centralmoments)
pool.add(namespace + '.' + 'temporal_spread', file_spread)#, pool.GlobalScope)
pool.add(namespace + '.' + 'temporal_skewness', file_skewness)#, pool.GlobalScope)
pool.add(namespace + '.' + 'temporal_kurtosis', file_kurtosis)#, pool.GlobalScope)
centroid = essentia.AudioCentroid(blockSize = len(audio))
pool.add(namespace + '.' + 'temporal_centroid', centroid(file_envelope))#, pool.GlobalScope)
# effective duration
effectiveduration = essentia.EffectiveDuration()
pool.add(namespace + '.' + 'effective_duration', effectiveduration(file_envelope))#, pool.GlobalScope)
# log attack time
logattacktime = essentia.LogAttackTime()
pool.add(namespace + '.' + 'logattacktime', logattacktime(audio))#, pool.GlobalScope)
# strong decay
strongdecay = essentia.StrongDecay()
pool.add(namespace + '.' + 'strongdecay', strongdecay(file_envelope))#, pool.GlobalScope)
# dynamic profile
flatness = essentia.FlatnessSFX()
pool.add(namespace + '.' + 'flatness', flatness(file_envelope))#, pool.GlobalScope)
# onsets number
onsets_number = len(pool.value('rhythm.onset_times')[0])
pool.add(namespace + '.' + 'onsets_number', onsets_number)#, pool.GlobalScope)
# morphological descriptors
max_to_total = essentia.MaxToTotal()
pool.add(namespace + '.' + 'max_to_total', max_to_total(file_envelope))#, pool.GlobalScope)
tc_to_total = essentia.TCToTotal(sampleRate = sampleRate)
pool.add(namespace + '.' + 'tc_to_total', tc_to_total(file_envelope))#, pool.GlobalScope)
derivativeSFX = essentia.DerivativeSFX(sampleRate = sampleRate)
(der_av_after_max, max_der_before_max) = derivativeSFX(file_envelope)
pool.add(namespace + '.' + 'der_av_after_max', der_av_after_max)#, pool.GlobalScope)
pool.add(namespace + '.' + 'max_der_before_max', max_der_before_max)#, pool.GlobalScope)
# pitch profile
pitch = pool.value('lowlevel.pitch')
if len(pitch) > 1:
pool.add(namespace + '.' + 'pitch_max_to_total', max_to_total(pitch))#, pool.GlobalScope)
min_to_total = essentia.MinToTotal()
pool.add(namespace + '.' + 'pitch_min_to_total', min_to_total(pitch))#, pool.GlobalScope)
pitch_centroid = essentia.Centroid(range = len(pitch)-1)
pool.add(namespace + '.' + 'pitch_centroid', pitch_centroid(pitch))#, pool.GlobalScope)
pitch_after_max_to_before_max_energy_ratio = essentia.AfterMaxToBeforeMaxEnergyRatio()
pool.add(namespace + '.' + 'pitch_after_max_to_before_max_energy_ratio', pitch_after_max_to_before_max_energy_ratio(pitch))#, pool.GlobalScope)
else:
pool.add(namespace + '.' + 'pitch_max_to_total', 0.0)#, pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_min_to_total', 0.0)#, pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_centroid', 0.0)#, pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_after_max_to_before_max_energy_ratio', 0.0)#, pool.GlobalScope)
progress.finish()
def compute(audio, pool, options):
INFO('Computing Tempo extractor...')
use_onset = options['useOnset']
use_bands = options['useBands']
# frameNumber * hopSize ~= about 6 seconds
hopSize = options['hopSize']
frameSize = options['frameSize']
frameNumber = options['frameNumber']
frameHop = options['frameHop']
sampleRate = options['sampleRate']
tolerance = 0.24
period_tol = 2
windowType = options['windowType']
bands_freq = [
40.0, 413.16, 974.51, 1818.94, 3089.19, 5000.0, 7874.4, 12198.29,
17181.13
]
bands_gain = [2.0, 3.0, 2.0, 1.0, 1.2, 2.0, 3.0, 2.5]
maxbpm = 208
minbpm = 40
last_beat_interval = 0.025
frame_time = float(hopSize) / float(sampleRate)
frames = essentia.FrameGenerator(audio=audio,
frameSize=frameSize,
hopSize=hopSize)
window = essentia.Windowing(size=frameSize, zeroPadding=0, type=windowType)
if use_onset:
fft = essentia.FFT(size=frameSize)
cartesian2polar = essentia.CartesianToPolar()
onset_hfc = essentia.OnsetDetection(method='hfc',
sampleRate=sampleRate)
onset_complex = essentia.OnsetDetection(method='complex',
sampleRate=sampleRate)
if use_bands:
espectrum = essentia.Spectrum(size=frameSize)
tempotapbands = essentia.FrequencyBands(frequencyBands=bands_freq)
temposcalebands = essentia.TempoScaleBands(bandsGain=bands_gain)
tempotap = essentia.TempoTap(numberFrames=frameNumber,
sampleRate=sampleRate,
frameHop=frameHop)
tempotapticks = essentia.TempoTapTicks(hopSize=hopSize,
sampleRate=sampleRate,
frameHop=frameHop)
frameTime = float(hopSize) / float(sampleRate)
frameRate = 1. / frameTime
nframes = 0
bpm_estimates_list = []
ticks = []
matchingPeriods = []
oldhfc = 0
fileLength = len(audio) / sampleRate
startSilence = 0
oldSilence = 0
endSilence = round(fileLength * sampleRate / hopSize) + 1
for frame in frames:
windowed_frame = window(frame)
features = []
if use_onset:
complex_fft = fft(windowed_frame)
(spectrum, phase) = cartesian2polar(complex_fft)
hfc = onset_hfc(spectrum, phase)
complexdomain = onset_complex(spectrum, phase)
difhfc = max(hfc - oldhfc, 0)
oldhfc = hfc
features += [hfc, difhfc, complexdomain]
if use_bands:
spectrum_frame = espectrum(windowed_frame)
bands = tempotapbands(spectrum_frame)
(scaled_bands, cumul) = temposcalebands(bands)
features += list(scaled_bands)
features = essentia.array(features)
(periods, phases) = tempotap(features)
(these_ticks, these_matchingPeriods) = tempotapticks(periods, phases)
for period in these_matchingPeriods:
if period != 0:
matchingPeriods += [period]
ticks += list(these_ticks)
if nframes < 5. * sampleRate / hopSize:
if isSilent(frame) and startSilence == nframes - 1:
startSilence = nframes
if nframes > (fileLength - 5.) * sampleRate / hopSize:
if isSilent(frame):
if oldSilence != nframes - 1:
endSilence = nframes
oldSilence = nframes
nframes += 1
# make sure we do not kill beat too close to music
if startSilence > 0: startSilence -= 1
endSilence += 1
# fill the rest of buffer with zeros
features = essentia.array([0] * len(features))
while nframes % frameNumber != 0:
(periods, phases) = tempotap(features)
(these_ticks, these_matchingPeriods) = tempotapticks(periods, phases)
ticks += list(these_ticks)
matchingPeriods += list(these_matchingPeriods)
nframes += 1
if len(ticks) > 2:
# fill up to end of file
if fileLength > ticks[-1]:
lastPeriod = ticks[-1] - ticks[-2]
while ticks[-1] + lastPeriod < fileLength - last_beat_interval:
if ticks[-1] > fileLength - last_beat_interval:
break
ticks.append(ticks[-1] + lastPeriod)
if len(ticks) > 1:
# remove all negative ticks
i = 0
while i < len(ticks):
if ticks[i] < startSilence / sampleRate * hopSize: ticks.pop(i)
else: i += 1
# kill all ticks from 350ms before the end of the song
i = 0
while i < len(ticks):
if ticks[i] > endSilence / sampleRate * hopSize: ticks.pop(i)
else: i += 1
# prune values closer than tolerance
i = 1
while i < len(ticks):
if ticks[i] - ticks[i - 1] < tolerance: ticks.pop(i)
else: i += 1
# prune all backward offbeat
i = 3
while i < len(ticks):
if abs( (ticks[i] - ticks[i-2]) - 1.5 * (ticks[i] - ticks[i-1]) ) < 0.100 \
and abs( (ticks[i] - ticks[i-1]) - (ticks[i-2] - ticks[i-3]) ) < 0.100 :
ticks.pop(i - 2)
else:
i += 1
for period in matchingPeriods:
if period != 0:
bpm_estimates_list += [lagtobpm(period, sampleRate, hopSize)]
#else:
# bpm_estimates_list += [ 0 ]
# bpm estimates
for bpm_estimate in bpm_estimates_list:
pool.add(namespace + '.' + 'bpm_estimates', bpm_estimate)
# estimate the bpm from the list of candidates
if len(bpm_estimates_list) > 0:
estimates = [bpm / 2. for bpm in bpm_estimates_list]
closestBpm = argmax(bincount(estimates)) * 2.
matching = []
for bpm in bpm_estimates_list:
if abs(closestBpm - bpm) < period_tol:
matching.append(bpm)
if (len(matching) < 1):
# something odd happened
bpm = closestBpm
else:
bpm = mean(matching)
else:
bpm = 0.
# convert to floats, as python bindings yet not support numpy.float32
ticks = [float(tick) for tick in ticks]
pool.add(namespace + '.' + 'bpm', bpm) #, pool.GlobalScope)
pool.add(namespace + '.' + 'beats_position', ticks) #, pool.GlobalScope
bpm_intervals = [ticks[i] - ticks[i - 1] for i in range(1, len(ticks))]
pool.add(namespace + '.' + 'bpm_intervals',
bpm_intervals) #, pool.GlobalScope
from numpy import histogram
tempotap_bpms = [60. / i for i in bpm_intervals]
if len(tempotap_bpms) > 0:
weight, values = histogram(tempotap_bpms,
bins=250,
range=(0, 250),
normed=True)
else:
weight, values = [0.], [0.]
first_peak_weights = [0] * 250
secnd_peak_weights = [0] * 250
for i in range(max(argmax(weight) - 4, 0),
min(argmax(weight) + 5, len(weight))):
first_peak_weights[i] = weight[i]
weight[i] = 0.
for i in range(max(argmax(weight) - 4, 0),
min(argmax(weight) + 5, len(weight))):
secnd_peak_weights[i] = weight[i]
weight[i] = 0.
pool.add(namespace + '.' + 'first_peak_bpm',
values[argmax(first_peak_weights)]) #, pool.GlobalScope
pool.add(
namespace + '.' + 'first_peak_weight',
first_peak_weights[argmax(first_peak_weights)]) #, pool.GlobalScope
if sum(first_peak_weights) != 0.:
pool.add(namespace + '.' + 'first_peak_spread',
1. - first_peak_weights[argmax(first_peak_weights)] /
sum(first_peak_weights)) #, pool.GlobalScope
else:
pool.add(namespace + '.' + 'first_peak_spread',
0.) #, pool.GlobalScope
pool.add(namespace + '.' + 'second_peak_bpm',
values[argmax(secnd_peak_weights)]) #, pool.GlobalScope
pool.add(
namespace + '.' + 'second_peak_weight',
secnd_peak_weights[argmax(secnd_peak_weights)]) #, pool.GlobalScope
if sum(secnd_peak_weights) != 0.:
pool.add(namespace + '.' + 'second_peak_spread',
1. - secnd_peak_weights[argmax(secnd_peak_weights)] /
sum(secnd_peak_weights)) #, pool.GlobalScope
else:
pool.add(namespace + '.' + 'second_peak_spread',
0.) #, pool.GlobalScope
'''
def rubato(ticks):
bpm_rubato_python = []
tolerance = 0.08
i = 5
tmp1 = 60./ float(ticks[i ] - ticks[i-1])
tmp2 = 60./ float(ticks[i-1] - ticks[i-2])
tmp3 = 60./ float(ticks[i-2] - ticks[i-3])
tmp4 = 60./ float(ticks[i-3] - ticks[i-4])
tmp5 = 60./ float(ticks[i-4] - ticks[i-5])
for i in range(6, len(ticks)):
if ( abs(1. - tmp1 / tmp4) >= tolerance
and abs(1. - tmp2 / tmp5) >= tolerance
and abs(1. - tmp2 / tmp4) >= tolerance
and abs(1. - tmp1 / tmp5) >= tolerance
and abs(1. - tmp1 / tmp2) <= tolerance
and abs(1. - tmp4 / tmp5) <= tolerance ):
bpm_rubato_python.append(ticks[i-2])
tmp5 = tmp4; tmp4 = tmp3; tmp3 = tmp2; tmp2 = tmp1
tmp1 = 60./ (ticks[i] - ticks[i-1])
print bpm_rubato_python
return bpm_rubato_python
'''
# FIXME we need better rubato algorithm
#rubato = essentia.BpmRubato()
#bpm_rubato_start, bpm_rubato_stop = rubato(ticks)
#pool.add(namespace + '.' + 'rubato_start', bpm_rubato_start)#, pool.GlobalScope
#pool.add(namespace + '.' + 'rubato_stop', bpm_rubato_stop)#, pool.GlobalScope)
INFO('100% done...')
def compute(audio, pool, options):
# analysis parameters
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
windowType = options['windowType']
# temporal descriptors
lpc = ess.LPC(order=10, type='warped', sampleRate=sampleRate)
zerocrossingrate = ess.ZeroCrossingRate()
# frame algorithms
frames = ess.FrameGenerator(audio=audio, frameSize=frameSize, hopSize=hopSize)
window = ess.Windowing(size=frameSize, zeroPadding=0, type=windowType)
spectrum = ess.Spectrum(size=frameSize)
# spectral algorithms
barkbands = ess.BarkBands(sampleRate=sampleRate)
centralmoments = ess.CentralMoments()
crest = ess.Crest()
centroid = ess.Centroid()
decrease = ess.Decrease()
spectral_contrast = ess.SpectralContrast(frameSize=frameSize,
sampleRate=sampleRate,
numberBands=6,
lowFrequencyBound=20,
highFrequencyBound=11000,
neighbourRatio=0.4,
staticDistribution=0.15)
distributionshape = ess.DistributionShape()
energy = ess.Energy()
# energyband_bass, energyband_middle and energyband_high parameters come from "standard" hi-fi equalizers
energyband_bass = ess.EnergyBand(startCutoffFrequency=20.0, stopCutoffFrequency=150.0, sampleRate=sampleRate)
energyband_middle_low = ess.EnergyBand(startCutoffFrequency=150.0, stopCutoffFrequency=800.0, sampleRate=sampleRate)
energyband_middle_high = ess.EnergyBand(startCutoffFrequency=800.0, stopCutoffFrequency=4000.0,
sampleRate=sampleRate)
energyband_high = ess.EnergyBand(startCutoffFrequency=4000.0, stopCutoffFrequency=20000.0, sampleRate=sampleRate)
flatnessdb = ess.FlatnessDB()
flux = ess.Flux()
harmonic_peaks = ess.HarmonicPeaks()
hfc = ess.HFC()
mfcc = ess.MFCC()
rolloff = ess.RollOff()
rms = ess.RMS()
strongpeak = ess.StrongPeak()
# pitch algorithms
pitch_detection = ess.PitchYinFFT(frameSize=frameSize, sampleRate=sampleRate)
pitch_salience = ess.PitchSalience()
# dissonance
spectral_peaks = ess.SpectralPeaks(sampleRate=sampleRate, orderBy='frequency')
dissonance = ess.Dissonance()
# spectral complexity
# magnitudeThreshold = 0.005 is hardcoded for a "blackmanharris62" frame
spectral_complexity = ess.SpectralComplexity(magnitudeThreshold=0.005)
INFO('Computing Low-Level descriptors...')
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize * 0.5
pitches, pitch_confidences = [], []
progress = Progress(total=total_frames)
#scPool = es.Pool() # pool for spectral contrast
for frame in frames:
frameScope = [start_of_frame / sampleRate, (start_of_frame + frameSize) / sampleRate]
# pool.setCurrentScope(frameScope)
# silence rate
# pool.add(namespace + '.' + 'silence_rate_60dB', es.isSilent(frame))
pool.add(namespace + '.' + 'silence_rate_60dB', is_silent_threshold(frame, -60))
pool.add(namespace + '.' + 'silence_rate_30dB', is_silent_threshold(frame, -30))
pool.add(namespace + '.' + 'silence_rate_20dB', is_silent_threshold(frame, -20))
if options['skipSilence'] and es.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
# temporal descriptors
pool.add(namespace + '.' + 'zerocrossingrate', zerocrossingrate(frame))
(frame_lpc, frame_lpc_reflection) = lpc(frame)
pool.add(namespace + '.' + 'temporal_lpc', frame_lpc)
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# spectrum-based descriptors
power_spectrum = frame_spectrum ** 2
pool.add(namespace + '.' + 'spectral_centroid', centroid(power_spectrum))
pool.add(namespace + '.' + 'spectral_decrease', decrease(power_spectrum))
pool.add(namespace + '.' + 'spectral_energy', energy(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_low', energyband_bass(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_middle_low', energyband_middle_low(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_middle_high', energyband_middle_high(frame_spectrum))
pool.add(namespace + '.' + 'spectral_energyband_high', energyband_high(frame_spectrum))
pool.add(namespace + '.' + 'hfc', hfc(frame_spectrum))
pool.add(namespace + '.' + 'spectral_rms', rms(frame_spectrum))
pool.add(namespace + '.' + 'spectral_flux', flux(frame_spectrum))
pool.add(namespace + '.' + 'spectral_rolloff', rolloff(frame_spectrum))
pool.add(namespace + '.' + 'spectral_strongpeak', strongpeak(frame_spectrum))
# central moments descriptors
frame_centralmoments = centralmoments(power_spectrum)
(frame_spread, frame_skewness, frame_kurtosis) = distributionshape(frame_centralmoments)
pool.add(namespace + '.' + 'spectral_kurtosis', frame_kurtosis)
pool.add(namespace + '.' + 'spectral_spread', frame_spread)
pool.add(namespace + '.' + 'spectral_skewness', frame_skewness)
# dissonance
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
frame_dissonance = dissonance(frame_frequencies, frame_magnitudes)
pool.add(namespace + '.' + 'dissonance', frame_dissonance)
# mfcc
(frame_melbands, frame_mfcc) = mfcc(frame_spectrum)
pool.add(namespace + '.' + 'mfcc', frame_mfcc)
# spectral contrast
(sc_coeffs, sc_valleys) = spectral_contrast(frame_spectrum)
#scPool.add(namespace + '.' + 'sccoeffs', sc_coeffs)
#scPool.add(namespace + '.' + 'scvalleys', sc_valleys)
pool.add(namespace + '.' + 'spectral_contrast', sc_coeffs)
# barkbands-based descriptors
frame_barkbands = barkbands(frame_spectrum)
pool.add(namespace + '.' + 'barkbands', frame_barkbands)
pool.add(namespace + '.' + 'spectral_crest', crest(frame_barkbands))
pool.add(namespace + '.' + 'spectral_flatness_db', flatnessdb(frame_barkbands))
barkbands_centralmoments = ess.CentralMoments(range=len(frame_barkbands) - 1)
(barkbands_spread, barkbands_skewness, barkbands_kurtosis) = distributionshape(
barkbands_centralmoments(frame_barkbands))
pool.add(namespace + '.' + 'barkbands_spread', barkbands_spread)
pool.add(namespace + '.' + 'barkbands_skewness', barkbands_skewness)
pool.add(namespace + '.' + 'barkbands_kurtosis', barkbands_kurtosis)
# pitch descriptors
frame_pitch, frame_pitch_confidence = pitch_detection(frame_spectrum)
if frame_pitch > 0 and frame_pitch <= 20000.:
pool.add(namespace + '.' + 'pitch', frame_pitch)
pitches.append(frame_pitch)
pitch_confidences.append(frame_pitch_confidence)
pool.add(namespace + '.' + 'pitch_instantaneous_confidence', frame_pitch_confidence)
frame_pitch_salience = pitch_salience(frame_spectrum[:-1])
pool.add(namespace + '.' + 'pitch_salience', frame_pitch_salience)
# spectral complexity
pool.add(namespace + '.' + 'spectral_complexity', spectral_complexity(frame_spectrum))
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
# if no 'temporal_zerocrossingrate' it means that this is a silent file
if 'zerocrossingrate' not in descriptorNames(pool.descriptorNames(), namespace):
raise ess.EssentiaError('This is a silent file!')
#spectralContrastPCA(scPool, pool)
# build pitch value histogram
from math import log
from numpy import bincount
# convert from Hz to midi notes
midipitches = []
unknown = 0
for freq in pitches:
if freq > 0. and freq <= 12600:
midipitches.append(12 * (log(freq / 6.875) / 0.69314718055995) - 3.)
else:
unknown += 1
if len(midipitches) > 0:
# compute histogram
midipitchhist = bincount(midipitches)
# set 0 midi pitch to be the number of pruned value
midipitchhist[0] = unknown
# normalise
midipitchhist = [val / float(sum(midipitchhist)) for val in midipitchhist]
# zero pad
for i in range(128 - len(midipitchhist)): midipitchhist.append(0.0)
else:
midipitchhist = [0.] * 128
midipitchhist[0] = 1.
# pitchhist = ess.array(zip(range(len(midipitchhist)), midipitchhist))
pool.add(namespace + '.' + 'spectral_pitch_histogram', midipitchhist) # , pool.GlobalScope)
# the code below is the same as the one above:
# for note in midipitchhist:
# pool.add(namespace + '.' + 'spectral_pitch_histogram_values', note)
# print "midi note:", note
pitch_centralmoments = ess.CentralMoments(range=len(midipitchhist) - 1)
(pitch_histogram_spread, pitch_histogram_skewness, pitch_histogram_kurtosis) = distributionshape(
pitch_centralmoments(midipitchhist))
pool.add(namespace + '.' + 'spectral_pitch_histogram_spread', pitch_histogram_spread) # , pool.GlobalScope)
progress.finish()
def compute(audio, pool, options):
INFO("Computing SFX descriptors...")
# analysis parameters
sampleRate = options["sampleRate"]
frameSize = options["frameSize"]
hopSize = options["hopSize"]
windowType = options["windowType"]
# frame algorithms
frames = ess.FrameGenerator(audio=audio, frameSize=frameSize, hopSize=hopSize)
window = ess.Windowing(size=frameSize, zeroPadding=0, type=windowType)
spectrum = ess.Spectrum(size=frameSize)
# pitch algorithm
pitch_detection = ess.PitchYinFFT(frameSize=2048, sampleRate=sampleRate)
# sfx descriptors
spectral_peaks = ess.SpectralPeaks(sampleRate=sampleRate, orderBy="frequency")
harmonic_peaks = ess.HarmonicPeaks()
inharmonicity = ess.Inharmonicity()
odd2evenharmonicenergyratio = ess.OddToEvenHarmonicEnergyRatio()
tristimulus = ess.Tristimulus()
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize * 0.5
progress = Progress(total=total_frames)
for frame in frames:
frameScope = [start_of_frame / sampleRate, (start_of_frame + frameSize) / sampleRate]
# pool.setCurrentScope(frameScope)
if options["skipSilence"] and es.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# pitch descriptors
frame_pitch, frame_pitch_confidence = pitch_detection(frame_spectrum)
# spectral peaks based descriptors
frame_frequencies, frame_magnitudes = spectral_peaks(frame_spectrum)
# ERROR CORRECTION - hoinx 2015-12
errIdx = np.where(frame_frequencies < 1)
frame_frequencies = np.delete(frame_frequencies, errIdx)
frame_magnitudes = np.delete(frame_magnitudes, errIdx)
(frame_harmonic_frequencies, frame_harmonic_magnitudes) = harmonic_peaks(
frame_frequencies, frame_magnitudes, frame_pitch
)
if len(frame_harmonic_frequencies) > 1:
frame_inharmonicity = inharmonicity(frame_harmonic_frequencies, frame_harmonic_magnitudes)
pool.add(namespace + "." + "inharmonicity", frame_inharmonicity)
frame_tristimulus = tristimulus(frame_harmonic_frequencies, frame_harmonic_magnitudes)
pool.add(namespace + "." + "tristimulus", frame_tristimulus)
frame_odd2evenharmonicenergyratio = odd2evenharmonicenergyratio(
frame_harmonic_frequencies, frame_harmonic_magnitudes
)
pool.add(namespace + "." + "odd2evenharmonicenergyratio", frame_odd2evenharmonicenergyratio)
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
envelope = ess.Envelope()
file_envelope = envelope(audio)
# temporal statistics
decrease = ess.Decrease()
pool.add(namespace + "." + "temporal_decrease", decrease(file_envelope)) # , pool.GlobalScope)
centralmoments = ess.CentralMoments()
file_centralmoments = centralmoments(file_envelope)
distributionshape = ess.DistributionShape()
(file_spread, file_skewness, file_kurtosis) = distributionshape(file_centralmoments)
pool.add(namespace + "." + "temporal_spread", file_spread) # , pool.GlobalScope)
pool.add(namespace + "." + "temporal_skewness", file_skewness) # , pool.GlobalScope)
pool.add(namespace + "." + "temporal_kurtosis", file_kurtosis) # , pool.GlobalScope)
centroid = ess.Centroid()
pool.add(namespace + "." + "temporal_centroid", centroid(file_envelope)) # , pool.GlobalScope)
# effective duration
effectiveduration = ess.EffectiveDuration()
pool.add(namespace + "." + "effective_duration", effectiveduration(file_envelope)) # , pool.GlobalScope)
# log attack time
logattacktime = ess.LogAttackTime()
pool.add(namespace + "." + "logattacktime", logattacktime(audio)) # , pool.GlobalScope)
# strong decay
strongdecay = ess.StrongDecay()
pool.add(namespace + "." + "strongdecay", strongdecay(file_envelope)) # , pool.GlobalScope)
# dynamic profile
flatness = ess.FlatnessSFX()
pool.add(namespace + "." + "flatness", flatness(file_envelope)) # , pool.GlobalScope)
"""
# onsets number
onsets_number = len(pool['rhythm.onset_times'][0])
pool.add(namespace + '.' + 'onsets_number', onsets_number) # , pool.GlobalScope)
"""
# morphological descriptors
max_to_total = ess.MaxToTotal()
pool.add(namespace + "." + "max_to_total", max_to_total(file_envelope)) # , pool.GlobalScope)
tc_to_total = ess.TCToTotal()
pool.add(namespace + "." + "tc_to_total", tc_to_total(file_envelope)) # , pool.GlobalScope)
derivativeSFX = ess.DerivativeSFX()
(der_av_after_max, max_der_before_max) = derivativeSFX(file_envelope)
pool.add(namespace + "." + "der_av_after_max", der_av_after_max) # , pool.GlobalScope)
pool.add(namespace + "." + "max_der_before_max", max_der_before_max) # , pool.GlobalScope)
# pitch profile
"""
pitch = pool['lowlevel.pitch']
if len(pitch) > 1:
pool.add(namespace + '.' + 'pitch_max_to_total', max_to_total(pitch)) # , pool.GlobalScope)
min_to_total = ess.MinToTotal()
pool.add(namespace + '.' + 'pitch_min_to_total', min_to_total(pitch)) # , pool.GlobalScope)
pitch_centroid = ess.Centroid(range=len(pitch) - 1)
pool.add(namespace + '.' + 'pitch_centroid', pitch_centroid(pitch)) # , pool.GlobalScope)
pitch_after_max_to_before_max_energy_ratio = ess.AfterMaxToBeforeMaxEnergyRatio()
pool.add(namespace + '.' + 'pitch_after_max_to_before_max_energy_ratio',
pitch_after_max_to_before_max_energy_ratio(pitch)) # , pool.GlobalScope)
else:
pool.add(namespace + '.' + 'pitch_max_to_total', 0.0) # , pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_min_to_total', 0.0) # , pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_centroid', 0.0) # , pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_after_max_to_before_max_energy_ratio', 0.0) # , pool.GlobalScope)
"""
progress.finish()
def compute(audio, pool, options):
INFO('Computing Tempo extractor...')
use_onset = options['useOnset']
use_bands = options['useBands']
# frameNumber * hopSize ~= about 6 seconds
hopSize = options['hopSize']
frameSize = options['frameSize']
frameNumber = options['frameNumber']
frameHop = options['frameHop']
sampleRate = options['sampleRate']
tolerance = 0.24
period_tol = 2
windowType = options['windowType']
bands_freq = [40.0, 413.16, 974.51, 1818.94, 3089.19, 5000.0, 7874.4, 12198.29, 17181.13]
bands_gain = [2.0, 3.0, 2.0, 1.0, 1.2, 2.0, 3.0, 2.5]
maxbpm = 208
minbpm = 40
last_beat_interval = 0.025
frame_time = float(hopSize) / float(sampleRate)
frames = essentia.FrameGenerator(audio = audio, frameSize = frameSize, hopSize = hopSize)
window = essentia.Windowing(size = frameSize, zeroPadding = 0, type = windowType)
if use_onset:
fft = essentia.FFT(size = frameSize)
cartesian2polar = essentia.CartesianToPolar()
onset_hfc = essentia.OnsetDetection(method = 'hfc', sampleRate = sampleRate)
onset_complex = essentia.OnsetDetection(method = 'complex', sampleRate = sampleRate)
if use_bands:
espectrum = essentia.Spectrum(size = frameSize)
tempotapbands = essentia.FrequencyBands(frequencyBands = bands_freq)
temposcalebands = essentia.TempoScaleBands(bandsGain = bands_gain)
tempotap = essentia.TempoTap(numberFrames = frameNumber, sampleRate = sampleRate, frameHop = frameHop)
tempotapticks = essentia.TempoTapTicks(hopSize = hopSize, sampleRate = sampleRate, frameHop = frameHop)
frameTime = float(hopSize) / float(sampleRate)
frameRate = 1. / frameTime
nframes = 0
bpm_estimates_list = []
ticks = []
matchingPeriods = []
oldhfc = 0
fileLength = len(audio)/sampleRate
startSilence = 0
oldSilence = 0
endSilence = round(fileLength * sampleRate / hopSize) + 1
for frame in frames:
windowed_frame = window(frame)
features = []
if use_onset:
complex_fft = fft(windowed_frame)
(spectrum,phase) = cartesian2polar(complex_fft)
hfc = onset_hfc(spectrum,phase)
complexdomain = onset_complex(spectrum,phase)
difhfc = max(hfc - oldhfc,0)
oldhfc = hfc
features += [hfc,difhfc,complexdomain]
if use_bands:
spectrum_frame = espectrum(windowed_frame)
bands = tempotapbands(spectrum_frame)
(scaled_bands, cumul) = temposcalebands(bands)
features += list(scaled_bands)
features = essentia.array(features)
(periods, phases) = tempotap(features)
(these_ticks, these_matchingPeriods) = tempotapticks(periods, phases)
for period in these_matchingPeriods:
if period != 0:
matchingPeriods += [ period ]
ticks += list(these_ticks)
if nframes < 5. * sampleRate / hopSize:
if isSilent(frame) and startSilence == nframes - 1:
startSilence = nframes
if nframes > (fileLength - 5.) * sampleRate / hopSize:
if isSilent(frame):
if oldSilence != nframes - 1:
endSilence = nframes
oldSilence = nframes
nframes += 1
# make sure we do not kill beat too close to music
if startSilence > 0: startSilence -= 1
endSilence += 1
# fill the rest of buffer with zeros
features = essentia.array([0]*len(features))
while nframes % frameNumber != 0:
(periods, phases) = tempotap(features)
(these_ticks, these_matchingPeriods) = tempotapticks(periods, phases)
ticks += list(these_ticks)
matchingPeriods += list(these_matchingPeriods)
nframes += 1
if len(ticks) > 2:
# fill up to end of file
if fileLength > ticks[-1]:
lastPeriod = ticks[-1] - ticks[-2]
while ticks[-1] + lastPeriod < fileLength - last_beat_interval:
if ticks[-1] > fileLength - last_beat_interval:
break
ticks.append(ticks[-1] + lastPeriod)
if len(ticks) > 1:
# remove all negative ticks
i = 0
while i < len(ticks):
if ticks[i] < startSilence / sampleRate * hopSize: ticks.pop(i)
else: i += 1
# kill all ticks from 350ms before the end of the song
i = 0
while i < len(ticks):
if ticks[i] > endSilence / sampleRate * hopSize: ticks.pop(i)
else: i += 1
# prune values closer than tolerance
i = 1
while i < len(ticks):
if ticks[i] - ticks[i-1] < tolerance: ticks.pop(i)
else: i += 1
# prune all backward offbeat
i = 3
while i < len(ticks):
if abs( (ticks[i] - ticks[i-2]) - 1.5 * (ticks[i] - ticks[i-1]) ) < 0.100 \
and abs( (ticks[i] - ticks[i-1]) - (ticks[i-2] - ticks[i-3]) ) < 0.100 :
ticks.pop(i-2)
else: i += 1
for period in matchingPeriods:
if period != 0:
bpm_estimates_list += [ lagtobpm(period, sampleRate, hopSize) ]
#else:
# bpm_estimates_list += [ 0 ]
# bpm estimates
for bpm_estimate in bpm_estimates_list:
pool.add(namespace + '.' + 'bpm_estimates', bpm_estimate)
# estimate the bpm from the list of candidates
if len(bpm_estimates_list) > 0:
estimates = [bpm/2. for bpm in bpm_estimates_list]
closestBpm = argmax(bincount(estimates))*2.
matching = []
for bpm in bpm_estimates_list:
if abs(closestBpm - bpm) < period_tol:
matching.append(bpm)
if (len(matching) < 1):
# something odd happened
bpm = closestBpm
else :
bpm = mean(matching)
else:
bpm = 0.
# convert to floats, as python bindings yet not support numpy.float32
ticks = [float(tick) for tick in ticks]
pool.add(namespace + '.' + 'bpm', bpm)#, pool.GlobalScope)
pool.add(namespace + '.' + 'beats_position', ticks)#, pool.GlobalScope
bpm_intervals = [ticks[i] - ticks[i-1] for i in range(1, len(ticks))]
pool.add(namespace + '.' + 'bpm_intervals', bpm_intervals)#, pool.GlobalScope
from numpy import histogram
tempotap_bpms = [60./i for i in bpm_intervals]
if len(tempotap_bpms) > 0:
weight, values = histogram(tempotap_bpms, bins = 250, range = (0,250), normed=True)
else:
weight, values = [0.], [0.]
first_peak_weights = [0] * 250
secnd_peak_weights = [0] * 250
for i in range(max(argmax(weight)-4,0), min(argmax(weight)+5,len(weight)) ):
first_peak_weights[i] = weight[i]
weight[i] = 0.
for i in range(max(argmax(weight)-4,0), min(argmax(weight)+5,len(weight)) ):
secnd_peak_weights[i] = weight[i]
weight[i] = 0.
pool.add(namespace + '.' + 'first_peak_bpm', values[argmax(first_peak_weights)])#, pool.GlobalScope
pool.add(namespace + '.' + 'first_peak_weight', first_peak_weights[argmax(first_peak_weights)])#, pool.GlobalScope
if sum(first_peak_weights) != 0.:
pool.add(namespace + '.' + 'first_peak_spread', 1.-first_peak_weights[argmax(first_peak_weights)]/sum(first_peak_weights))#, pool.GlobalScope
else:
pool.add(namespace + '.' + 'first_peak_spread', 0.)#, pool.GlobalScope
pool.add(namespace + '.' + 'second_peak_bpm', values[argmax(secnd_peak_weights)])#, pool.GlobalScope
pool.add(namespace + '.' + 'second_peak_weight', secnd_peak_weights[argmax(secnd_peak_weights)])#, pool.GlobalScope
if sum(secnd_peak_weights) != 0.:
pool.add(namespace + '.' + 'second_peak_spread', 1.-secnd_peak_weights[argmax(secnd_peak_weights)]/sum(secnd_peak_weights))#, pool.GlobalScope
else:
pool.add(namespace + '.' + 'second_peak_spread', 0.)#, pool.GlobalScope
'''
def rubato(ticks):
bpm_rubato_python = []
tolerance = 0.08
i = 5
tmp1 = 60./ float(ticks[i ] - ticks[i-1])
tmp2 = 60./ float(ticks[i-1] - ticks[i-2])
tmp3 = 60./ float(ticks[i-2] - ticks[i-3])
tmp4 = 60./ float(ticks[i-3] - ticks[i-4])
tmp5 = 60./ float(ticks[i-4] - ticks[i-5])
for i in range(6, len(ticks)):
if ( abs(1. - tmp1 / tmp4) >= tolerance
and abs(1. - tmp2 / tmp5) >= tolerance
and abs(1. - tmp2 / tmp4) >= tolerance
and abs(1. - tmp1 / tmp5) >= tolerance
and abs(1. - tmp1 / tmp2) <= tolerance
and abs(1. - tmp4 / tmp5) <= tolerance ):
bpm_rubato_python.append(ticks[i-2])
tmp5 = tmp4; tmp4 = tmp3; tmp3 = tmp2; tmp2 = tmp1
tmp1 = 60./ (ticks[i] - ticks[i-1])
print bpm_rubato_python
return bpm_rubato_python
'''
# FIXME we need better rubato algorithm
#rubato = essentia.BpmRubato()
#bpm_rubato_start, bpm_rubato_stop = rubato(ticks)
#pool.add(namespace + '.' + 'rubato_start', bpm_rubato_start)#, pool.GlobalScope
#pool.add(namespace + '.' + 'rubato_stop', bpm_rubato_stop)#, pool.GlobalScope)
INFO('100% done...')
def compute(audio, pool, options):
INFO('Computing Tonal descriptors...')
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
zeroPadding = options['zeroPadding']
windowType = options['windowType']
frames = essentia.FrameGenerator(audio=audio,
frameSize=frameSize,
hopSize=hopSize)
window = essentia.Windowing(size=frameSize,
zeroPadding=zeroPadding,
type=windowType)
spectrum = essentia.Spectrum(size=(frameSize + zeroPadding) / 2)
spectral_peaks = essentia.SpectralPeaks(maxPeaks=10000,
magnitudeThreshold=0.00001,
minFrequency=40,
maxFrequency=5000,
orderBy="frequency")
tuning = essentia.TuningFrequency()
# computing the tuning frequency
tuning_frequency = 440.0
for frame in frames:
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
#if len(frame_frequencies) > 0:
(tuning_frequency, tuning_cents) = tuning(frame_frequencies,
frame_magnitudes)
pool.add(namespace + '.' + 'tuning_frequency',
tuning_frequency) #, pool.GlobalScope)
# computing the HPCPs
spectral_whitening = essentia.SpectralWhitening()
hpcp_key_size = 36
hpcp_chord_size = 36
hpcp_tuning_size = 120
hpcp_key = essentia.HPCP(size=hpcp_key_size,
referenceFrequency=tuning_frequency,
bandPreset=False,
minFrequency=40.0,
maxFrequency=5000.0,
weightType='squaredCosine',
nonLinear=False,
windowSize=4.0 / 3.0,
sampleRate=sampleRate)
hpcp_chord = essentia.HPCP(size=hpcp_chord_size,
referenceFrequency=tuning_frequency,
harmonics=8,
bandPreset=True,
minFrequency=40.0,
maxFrequency=5000.0,
splitFrequency=500.0,
weightType='cosine',
nonLinear=True,
windowSize=0.5,
sampleRate=sampleRate)
hpcp_tuning = essentia.HPCP(size=hpcp_tuning_size,
referenceFrequency=tuning_frequency,
harmonics=8,
bandPreset=True,
minFrequency=40.0,
maxFrequency=5000.0,
splitFrequency=500.0,
weightType='cosine',
nonLinear=True,
windowSize=0.5,
sampleRate=sampleRate)
# intializing the HPCP arrays
hpcps_key = []
hpcps_chord = []
hpcps_tuning = []
# computing HPCP loop
frames = essentia.FrameGenerator(audio=audio,
frameSize=frameSize,
hopSize=hopSize)
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize * 0.5
progress = Progress(total=total_frames)
for frame in frames:
#frameScope = [ start_of_frame / sampleRate, (start_of_frame + frameSize) / sampleRate ]
#pool.setCurrentScope(frameScope)
if options['skipSilence'] and essentia.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# spectral peaks
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
if (len(frame_frequencies) > 0):
# spectral_whitening
frame_magnitudes_white = spectral_whitening(
frame_spectrum, frame_frequencies, frame_magnitudes)
frame_hpcp_key = hpcp_key(frame_frequencies,
frame_magnitudes_white)
frame_hpcp_chord = hpcp_chord(frame_frequencies,
frame_magnitudes_white)
frame_hpcp_tuning = hpcp_tuning(frame_frequencies,
frame_magnitudes_white)
else:
frame_hpcp_key = essentia.array([0] * hpcp_key_size)
frame_hpcp_chord = essentia.array([0] * hpcp_chord_size)
frame_hpcp_tuning = essentia.array([0] * hpcp_tuning_size)
# key HPCP
hpcps_key.append(frame_hpcp_key)
# add HPCP to the pool
pool.add(namespace + '.' + 'hpcp', frame_hpcp_key)
# chords HPCP
hpcps_chord.append(frame_hpcp_chord)
# tuning system HPCP
hpcps_tuning.append(frame_hpcp_tuning)
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
progress.finish()
# check if silent file
if len(hpcps_key) == 0:
raise EssentiaError('This is a silent file!')
# key detection
key_detector = essentia.Key(profileType='temperley')
average_hpcps_key = numpy.average(essentia.array(hpcps_key), axis=0)
average_hpcps_key = normalize(average_hpcps_key)
# thpcps
max_arg = numpy.argmax(average_hpcps_key)
thpcp = []
for i in range(max_arg, len(average_hpcps_key)):
thpcp.append(float(average_hpcps_key[i]))
for i in range(max_arg):
thpcp.append(float(average_hpcps_key[i]))
pool.add(namespace + '.' + 'thpcp', thpcp) #, pool.GlobalScope )
(key, scale, key_strength,
first_to_second_relative_strength) = key_detector(
essentia.array(average_hpcps_key))
pool.add(namespace + '.' + 'key_key', key) #, pool.GlobalScope)
pool.add(namespace + '.' + 'key_scale', scale) #, pool.GlobalScope)
pool.add(namespace + '.' + 'key_strength',
key_strength) #, pool.GlobalScope)
# chord detection
chord_detector = essentia.Key(profileType='tonictriad', usePolyphony=False)
hpcp_frameSize = 2.0 # 2 seconds
hpcp_number = int(hpcp_frameSize * (sampleRate / hopSize - 1))
for hpcp_index in range(len(hpcps_chord)):
hpcp_index_begin = max(0, hpcp_index - hpcp_number)
hpcp_index_end = min(hpcp_index + hpcp_number, len(hpcps_chord))
average_hpcps_chord = numpy.average(essentia.array(
hpcps_chord[hpcp_index_begin:hpcp_index_end]),
axis=0)
average_hpcps_chord = normalize(average_hpcps_chord)
(key, scale, strength,
first_to_second_relative_strength) = chord_detector(
essentia.array(average_hpcps_chord))
if scale == 'minor':
chord = key + 'm'
else:
chord = key
frame_second_scope = [
hpcp_index_begin * hopSize / sampleRate,
hpcp_index_end * hopSize / sampleRate
]
pool.add(namespace + '.' + 'chords_progression',
chord) #, frame_second_scope)
pool.add(namespace + '.' + 'chords_strength',
strength) #, frame_second_scope)
# tuning system features
keydetector = essentia.Key(profileType='diatonic')
average_hpcps_tuning = numpy.average(essentia.array(hpcps_tuning), axis=0)
average_hpcps_tuning = normalize(average_hpcps_tuning)
(key, scale, diatonic_strength,
first_to_second_relative_strength) = keydetector(
essentia.array(average_hpcps_tuning))
pool.add(namespace + '.' + 'tuning_diatonic_strength',
diatonic_strength) #, pool.GlobalScope)
(equal_tempered_deviation, nontempered_energy_ratio,
nontempered_peaks_energy_ratio
) = essentia.HighResolutionFeatures()(average_hpcps_tuning)
pool.add(namespace + '.' + 'tuning_equal_tempered_deviation',
equal_tempered_deviation) #, pool.GlobalScope)
pool.add(namespace + '.' + 'tuning_nontempered_energy_ratio',
nontempered_energy_ratio) #, pool.GlobalScope)
pool.add(namespace + '.' + 'tuning_nontempered_peaks_energy_ratio',
nontempered_peaks_energy_ratio) #, pool.GlobalScope)
def compute(audio, pool, options):
INFO('Computing Tonal descriptors...')
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
zeroPadding = options['zeroPadding']
windowType = options['windowType']
frames = essentia.FrameGenerator(audio = audio, frameSize = frameSize, hopSize = hopSize)
window = essentia.Windowing(size = frameSize, zeroPadding = zeroPadding, type = windowType)
spectrum = essentia.Spectrum(size = (frameSize + zeroPadding) / 2)
spectral_peaks = essentia.SpectralPeaks(maxPeaks = 10000, magnitudeThreshold = 0.00001, minFrequency = 40, maxFrequency = 5000, orderBy = "frequency")
tuning = essentia.TuningFrequency()
# computing the tuning frequency
tuning_frequency = 440.0
for frame in frames:
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
#if len(frame_frequencies) > 0:
(tuning_frequency, tuning_cents) = tuning(frame_frequencies, frame_magnitudes)
pool.add(namespace + '.' + 'tuning_frequency', tuning_frequency)#, pool.GlobalScope)
# computing the HPCPs
spectral_whitening = essentia.SpectralWhitening()
hpcp_key_size = 36
hpcp_chord_size = 36
hpcp_tuning_size = 120
hpcp_key = essentia.HPCP(size = hpcp_key_size,
referenceFrequency = tuning_frequency,
bandPreset = False,
minFrequency = 40.0,
maxFrequency = 5000.0,
weightType = 'squaredCosine',
nonLinear = False,
windowSize = 4.0/3.0,
sampleRate = sampleRate)
hpcp_chord = essentia.HPCP(size = hpcp_chord_size,
referenceFrequency = tuning_frequency,
harmonics = 8,
bandPreset = True,
minFrequency = 40.0,
maxFrequency = 5000.0,
splitFrequency = 500.0,
weightType = 'cosine',
nonLinear = True,
windowSize = 0.5,
sampleRate = sampleRate)
hpcp_tuning = essentia.HPCP(size = hpcp_tuning_size,
referenceFrequency = tuning_frequency,
harmonics = 8,
bandPreset = True,
minFrequency = 40.0,
maxFrequency = 5000.0,
splitFrequency = 500.0,
weightType = 'cosine',
nonLinear = True,
windowSize = 0.5,
sampleRate = sampleRate)
# intializing the HPCP arrays
hpcps_key = []
hpcps_chord = []
hpcps_tuning = []
# computing HPCP loop
frames = essentia.FrameGenerator(audio = audio, frameSize = frameSize, hopSize = hopSize)
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize * 0.5
progress = Progress(total = total_frames)
for frame in frames:
#frameScope = [ start_of_frame / sampleRate, (start_of_frame + frameSize) / sampleRate ]
#pool.setCurrentScope(frameScope)
if options['skipSilence'] and essentia.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# spectral peaks
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
if (len(frame_frequencies) > 0):
# spectral_whitening
frame_magnitudes_white = spectral_whitening(frame_spectrum, frame_frequencies, frame_magnitudes)
frame_hpcp_key = hpcp_key(frame_frequencies, frame_magnitudes_white)
frame_hpcp_chord = hpcp_chord(frame_frequencies, frame_magnitudes_white)
frame_hpcp_tuning = hpcp_tuning(frame_frequencies, frame_magnitudes_white)
else:
frame_hpcp_key = essentia.array([0] * hpcp_key_size)
frame_hpcp_chord = essentia.array([0] * hpcp_chord_size)
frame_hpcp_tuning = essentia.array([0] * hpcp_tuning_size)
# key HPCP
hpcps_key.append(frame_hpcp_key)
# add HPCP to the pool
pool.add(namespace + '.' +'hpcp', frame_hpcp_key)
# chords HPCP
hpcps_chord.append(frame_hpcp_chord)
# tuning system HPCP
hpcps_tuning.append(frame_hpcp_tuning)
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
progress.finish()
# check if silent file
if len(hpcps_key) == 0:
raise EssentiaError('This is a silent file!')
# key detection
key_detector = essentia.Key(profileType = 'temperley')
average_hpcps_key = numpy.average(essentia.array(hpcps_key), axis=0)
average_hpcps_key = normalize(average_hpcps_key)
# thpcps
max_arg = numpy.argmax( average_hpcps_key )
thpcp=[]
for i in range( max_arg, len(average_hpcps_key) ):
thpcp.append( float(average_hpcps_key[i]) )
for i in range( max_arg ):
thpcp.append( float(average_hpcps_key[i]) )
pool.add(namespace + '.' +'thpcp', thpcp)#, pool.GlobalScope )
(key, scale, key_strength, first_to_second_relative_strength) = key_detector(essentia.array(average_hpcps_key))
pool.add(namespace + '.' +'key_key', key)#, pool.GlobalScope)
pool.add(namespace + '.' +'key_scale', scale)#, pool.GlobalScope)
pool.add(namespace + '.' +'key_strength', key_strength)#, pool.GlobalScope)
# chord detection
chord_detector = essentia.Key(profileType = 'tonictriad', usePolyphony = False)
hpcp_frameSize = 2.0 # 2 seconds
hpcp_number = int(hpcp_frameSize * (sampleRate / hopSize - 1))
for hpcp_index in range(len(hpcps_chord)):
hpcp_index_begin = max(0, hpcp_index - hpcp_number)
hpcp_index_end = min(hpcp_index + hpcp_number, len(hpcps_chord))
average_hpcps_chord = numpy.average(essentia.array(hpcps_chord[hpcp_index_begin : hpcp_index_end]), axis=0)
average_hpcps_chord = normalize(average_hpcps_chord)
(key, scale, strength, first_to_second_relative_strength) = chord_detector(essentia.array(average_hpcps_chord))
if scale == 'minor':
chord = key + 'm'
else:
chord = key
frame_second_scope = [hpcp_index_begin * hopSize / sampleRate, hpcp_index_end * hopSize / sampleRate]
pool.add(namespace + '.' +'chords_progression', chord)#, frame_second_scope)
pool.add(namespace + '.' +'chords_strength', strength)#, frame_second_scope)
# tuning system features
keydetector = essentia.Key(profileType = 'diatonic')
average_hpcps_tuning = numpy.average(essentia.array(hpcps_tuning), axis=0)
average_hpcps_tuning = normalize(average_hpcps_tuning)
(key, scale, diatonic_strength, first_to_second_relative_strength) = keydetector(essentia.array(average_hpcps_tuning))
pool.add(namespace + '.' +'tuning_diatonic_strength', diatonic_strength)#, pool.GlobalScope)
(equal_tempered_deviation,
nontempered_energy_ratio,
nontempered_peaks_energy_ratio) = essentia.HighResolutionFeatures()(average_hpcps_tuning)
pool.add(namespace + '.' +'tuning_equal_tempered_deviation', equal_tempered_deviation)#, pool.GlobalScope)
pool.add(namespace + '.' +'tuning_nontempered_energy_ratio', nontempered_energy_ratio)#, pool.GlobalScope)
pool.add(namespace + '.' +'tuning_nontempered_peaks_energy_ratio', nontempered_peaks_energy_ratio)#, pool.GlobalScope)
def compute(audio, pool, options):
INFO('Computing SFX descriptors...')
# analysis parameters
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
windowType = options['windowType']
# frame algorithms
frames = essentia.FrameGenerator(audio=audio,
frameSize=frameSize,
hopSize=hopSize)
window = essentia.Windowing(size=frameSize, zeroPadding=0, type=windowType)
spectrum = essentia.Spectrum(size=frameSize)
# pitch algorithm
pitch_detection = essentia.PitchDetection(frameSize=2048,
sampleRate=sampleRate)
# sfx descriptors
spectral_peaks = essentia.SpectralPeaks(sampleRate=sampleRate,
orderBy='frequency')
harmonic_peaks = essentia.HarmonicPeaks()
inharmonicity = essentia.Inharmonicity()
odd2evenharmonicenergyratio = essentia.OddToEvenHarmonicEnergyRatio()
tristimulus = essentia.Tristimulus()
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize * 0.5
progress = Progress(total=total_frames)
for frame in frames:
frameScope = [
start_of_frame / sampleRate,
(start_of_frame + frameSize) / sampleRate
]
#pool.setCurrentScope(frameScope)
if options['skipSilence'] and essentia.isSilent(frame):
total_frames -= 1
start_of_frame += hopSize
continue
frame_windowed = window(frame)
frame_spectrum = spectrum(frame_windowed)
# pitch descriptors
frame_pitch, frame_pitch_confidence = pitch_detection(frame_spectrum)
# spectral peaks based descriptors
(frame_frequencies, frame_magnitudes) = spectral_peaks(frame_spectrum)
(frame_harmonic_frequencies,
frame_harmonic_magnitudes) = harmonic_peaks(frame_frequencies,
frame_magnitudes,
frame_pitch)
if len(frame_harmonic_frequencies) > 1:
frame_inharmonicity = inharmonicity(frame_harmonic_frequencies,
frame_harmonic_magnitudes)
pool.add(namespace + '.' + 'inharmonicity', frame_inharmonicity)
frame_tristimulus = tristimulus(frame_harmonic_frequencies,
frame_harmonic_magnitudes)
pool.add(namespace + '.' + 'tristimulus', frame_tristimulus)
frame_odd2evenharmonicenergyratio = odd2evenharmonicenergyratio(
frame_harmonic_frequencies, frame_harmonic_magnitudes)
pool.add(namespace + '.' + 'odd2evenharmonicenergyratio',
frame_odd2evenharmonicenergyratio)
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
envelope = essentia.Envelope()
file_envelope = envelope(audio)
# temporal statistics
decrease = essentia.AudioDecrease(blockSize=len(audio))
pool.add(namespace + '.' + 'temporal_decrease',
decrease(file_envelope)) #, pool.GlobalScope)
centralmoments = essentia.AudioCentralMoments(blockSize=len(audio))
file_centralmoments = centralmoments(file_envelope)
distributionshape = essentia.DistributionShape()
(file_spread, file_skewness,
file_kurtosis) = distributionshape(file_centralmoments)
pool.add(namespace + '.' + 'temporal_spread',
file_spread) #, pool.GlobalScope)
pool.add(namespace + '.' + 'temporal_skewness',
file_skewness) #, pool.GlobalScope)
pool.add(namespace + '.' + 'temporal_kurtosis',
file_kurtosis) #, pool.GlobalScope)
centroid = essentia.AudioCentroid(blockSize=len(audio))
pool.add(namespace + '.' + 'temporal_centroid',
centroid(file_envelope)) #, pool.GlobalScope)
# effective duration
effectiveduration = essentia.EffectiveDuration()
pool.add(namespace + '.' + 'effective_duration',
effectiveduration(file_envelope)) #, pool.GlobalScope)
# log attack time
logattacktime = essentia.LogAttackTime()
pool.add(namespace + '.' + 'logattacktime',
logattacktime(audio)) #, pool.GlobalScope)
# strong decay
strongdecay = essentia.StrongDecay()
pool.add(namespace + '.' + 'strongdecay',
strongdecay(file_envelope)) #, pool.GlobalScope)
# dynamic profile
flatness = essentia.FlatnessSFX()
pool.add(namespace + '.' + 'flatness',
flatness(file_envelope)) #, pool.GlobalScope)
# onsets number
onsets_number = len(pool.value('rhythm.onset_times')[0])
pool.add(namespace + '.' + 'onsets_number',
onsets_number) #, pool.GlobalScope)
# morphological descriptors
max_to_total = essentia.MaxToTotal()
pool.add(namespace + '.' + 'max_to_total',
max_to_total(file_envelope)) #, pool.GlobalScope)
tc_to_total = essentia.TCToTotal(sampleRate=sampleRate)
pool.add(namespace + '.' + 'tc_to_total',
tc_to_total(file_envelope)) #, pool.GlobalScope)
derivativeSFX = essentia.DerivativeSFX(sampleRate=sampleRate)
(der_av_after_max, max_der_before_max) = derivativeSFX(file_envelope)
pool.add(namespace + '.' + 'der_av_after_max',
der_av_after_max) #, pool.GlobalScope)
pool.add(namespace + '.' + 'max_der_before_max',
max_der_before_max) #, pool.GlobalScope)
# pitch profile
pitch = pool.value('lowlevel.pitch')
if len(pitch) > 1:
pool.add(namespace + '.' + 'pitch_max_to_total',
max_to_total(pitch)) #, pool.GlobalScope)
min_to_total = essentia.MinToTotal()
pool.add(namespace + '.' + 'pitch_min_to_total',
min_to_total(pitch)) #, pool.GlobalScope)
pitch_centroid = essentia.Centroid(range=len(pitch) - 1)
pool.add(namespace + '.' + 'pitch_centroid',
pitch_centroid(pitch)) #, pool.GlobalScope)
pitch_after_max_to_before_max_energy_ratio = essentia.AfterMaxToBeforeMaxEnergyRatio(
)
pool.add(namespace + '.' +
'pitch_after_max_to_before_max_energy_ratio',
pitch_after_max_to_before_max_energy_ratio(
pitch)) #, pool.GlobalScope)
else:
pool.add(namespace + '.' + 'pitch_max_to_total',
0.0) #, pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_min_to_total',
0.0) #, pool.GlobalScope)
pool.add(namespace + '.' + 'pitch_centroid', 0.0) #, pool.GlobalScope)
pool.add(namespace + '.' +
'pitch_after_max_to_before_max_energy_ratio',
0.0) #, pool.GlobalScope)
progress.finish()
