def compute(pool, options):
minimumLength = options['segmentation']['minimumSegmentsLength']
energy = pool.descriptors['lowlevel']['spectral_rms']['values']
scopes = pool.descriptors['lowlevel']['spectral_rms']['scopes']
energyScopes = []
for scope in scopes:
energyScopes.append(scope[0])
energyHop = energyScopes[1] - energyScopes[0]
hopSizeDuration = 1.0
hopSize = hopSizeDuration / energyHop
frameSizeDuration = minimumLength
frameSize = frameSizeDuration / energyHop
frames = essentia.FrameGenerator(audio=energy,
frameSize=frameSize,
hopSize=hopSize,
startFromZero=True)
framesEnergy = []
for frame in frames:
framesEnergy.append(sum(frame))
maxFrameIndex = framesEnergy.index(max(framesEnergy))
onsetStart = energyScopes[int(maxFrameIndex * hopSize)]
onsetEnd = onsetStart + frameSizeDuration
onsets = [onsetStart, onsetEnd]
return onsets
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()
import numpy
sample_rate = 44100.0
frame_size = 32768
hop_size = 16384
frame_rate = (frame_size - hop_size) / sample_rate
zero_padding = 0
all_files = glob.glob('../../../test/audio/patterns/*.mp3')
for input_file in all_files:
audio = essentia.AudioFileInput(filename=input_file)
samples = audio()
frames = essentia.FrameGenerator(audio=samples,
frameSize=frame_size,
hopSize=hop_size)
window = essentia.Windowing(windowSize=frame_size,
zeroPadding=zero_padding,
type="blackmanharris62")
spectrum = essentia.Spectrum(size=frame_size + zero_padding)
spectralpeaks = essentia.SpectralPeaks(maxPeaks=100,
magnitudeThreshold=-100,
minFrequency=130,
maxFrequency=8000,
highestPeaks=True)
lin2db = essentia.UnaryOperator(type="lin2db")
db2lin = essentia.UnaryOperator(type="db2lin")
hpcp = essentia.HPCP(size=12,
referenceFrequency=440,
useWeight=True,
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 = 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):
# 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,
startFromZero=True)
loudness = essentia.Loudness()
INFO('Computing Dynamic descriptors...')
# used for a nice progress display
total_frames = frames.num_frames()
n_frames = 0
level_array = []
progress = Progress(total=total_frames)
for frame in frames:
frame_level = loudness(frame)
level_array.append(frame_level)
# display of progress report
progress.update(n_frames)
n_frames += 1
# Maximum dynamic
EPSILON = 10e-6
max_value = max(level_array)
if max_value <= EPSILON:
max_value = EPSILON
# Normalization to the maximum
THRESHOLD = 0.0001 # this corresponds to -80dB
for i in range(len(level_array)):
level_array[i] /= max_value
if level_array[i] <= THRESHOLD:
level_array[i] = THRESHOLD
# Dynamic Average
mean = essentia.Mean()
average_loudness = 10.0 * log10(mean(level_array))
# re-scaling and range-control
# This yields in numbers between
#
# 0 for signals with large dynamic variace and
# thus low dynamic average
# 1 for signal with little dynamic range and thus
# a dynamic average close to the maximum
# TO DO: [0, 0] should be pool.GlobalScope
average_loudness_within_zero_to_one = squeezeInto([-5, 0], [-2, 1],
average_loudness)
pool.add(namespace + "." + "average_loudness",
average_loudness_within_zero_to_one) #, pool.GlobalScope)
# Dynamic Fluctuation
'''
variance = essentia.Variance()
level_variance = variance(level_array)
if level_variance <= THRESHOLD:
level_variance = THRESHOLD
level_fluctuation = 10*log10(level_variance)
# TO DO: [0, 0] should be pool.GlobalScope
pool.add("level_fluctuation", level_fluctuation, pool.GlobalScope)
'''
INFO('\r100% 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):
sampleRate = options['sampleRate']
frameSize = options['frameSize']
hopSize = options['hopSize']
zeroPadding = options['zeroPadding']
windowType = options['windowType']
frameRate = float(sampleRate) / float(frameSize - hopSize)
INFO('Computing Onset Detection...')
frames = essentia.FrameGenerator(audio=audio,
frameSize=frameSize,
hopSize=hopSize)
window = essentia.Windowing(size=frameSize,
zeroPadding=zeroPadding,
type=windowType)
fft = essentia.FFT()
cartesian2polar = essentia.CartesianToPolar()
onsetdetectionHFC = essentia.OnsetDetection(method="hfc",
sampleRate=sampleRate)
onsetdetectionComplex = essentia.OnsetDetection(method="complex",
sampleRate=sampleRate)
onsets = essentia.Onsets(frameRate=frameRate)
total_frames = frames.num_frames()
n_frames = 0
start_of_frame = -frameSize * 0.5
hfc = []
complex = []
progress = Progress(total=total_frames)
for frame in frames:
if essentia.instantPower(frame) < 1.e-4:
total_frames -= 1
start_of_frame += hopSize
hfc.append(0.)
complex.append(0.)
continue
windowed_frame = window(frame)
complex_fft = fft(windowed_frame)
(spectrum, phase) = cartesian2polar(complex_fft)
hfc.append(onsetdetectionHFC(spectrum, phase))
complex.append(onsetdetectionComplex(spectrum, phase))
# display of progress report
progress.update(n_frames)
n_frames += 1
start_of_frame += hopSize
# The onset rate is defined as the number of onsets per seconds
detections = numpy.concatenate(
[essentia.array([hfc]),
essentia.array([complex])])
# prune all 'doubled' detections
time_onsets = list(onsets(detections, essentia.array([1, 1])))
t = 1
while t < len(time_onsets):
if time_onsets[t] - time_onsets[t - 1] < 0.080: time_onsets.pop(t)
else: t += 1
onsetrate = len(time_onsets) / (len(audio) / sampleRate)
pool.add(namespace + '.' + "onset_times",
essentia.array(time_onsets)) #, pool.GlobalScope)
pool.add(namespace + '.' + "onset_rate", onsetrate) #, 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()
