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 Beats descriptors...")
sampleRate = options['sampleRate']
windowType = options['windowType']
beat_window_duration = 0.1 # 100ms
beat_duration = 0.05 # 50ms estimation after checking some drums kicks duration on freesound
beats = pool.value('rhythm.beats_position')[0]
# special case
if len(beats) == 0:
# we add them 2 times to get 'mean/var' stats and not 'value'
# and not on full scope so it's not global
# FIXME: should use "undefined"
pool.add("beats_loudness", 0.0, [0., 0.])
pool.add("beats_loudness", 0.0, [0., 0.])
pool.add("beats_loudness_bass", 0.0, [0., 0.])
pool.add("beats_loudness_bass", 0.0, [0., 0.])
INFO('100% done...')
return
duration = pool.value('metadata.duration_processed')[0]
# FIXME: converted to samples in order to have more accurate control of the size of
# the window. This is due to FFT not being able to be computed on arrays of
# odd sizes. Please FIXME later, when FFT accepts all kinds of sizes.
beat_window_duration = int(beat_window_duration * float(sampleRate) + 0.5)
beat_duration = int(beat_duration * float(sampleRate) + 0.5)
duration *= float(sampleRate)
if beat_duration % 2 == 1:
beat_duration += 1
beat_window_duration = beat_duration * 2
energy = essentia.Energy()
energybandratio = essentia.EnergyBandRatio(startFrequency=20.0,
stopFrequency=150.0,
sampleRate=sampleRate)
total_beats = len(beats)
n_beats = 1
progress = Progress(total=total_beats)
between_beats_start = [0.0]
between_beats_end = []
beats_spectral_energy = 0.0
# love on the beats
for beat in beats:
# convert beat to samples in order to ensure an even size
beat = beat * float(sampleRate)
beat_window_start = (beat - beat_window_duration / 2.0) # in samples
beat_window_end = (beat + beat_window_duration / 2.0) # in samples
if beat_window_start > 0.0 and beat_window_end < duration: # in samples
#print "duration: ", duration, "start:", beat_window_start, "end:", beat_window_end
beat_window = audio[beat_window_start:beat_window_end]
beat_start = beat_window_start + max_energy_index(beat_window)
beat_end = beat_start + beat_duration
beat_audio = audio[beat_start:beat_end]
beat_scope = [
beat_start / float(sampleRate), beat_end / float(sampleRate)
] # in seconds
#print "beat audio size: ", len(beat_audio)
window = essentia.Windowing(size=len(beat_audio),
zeroPadding=0,
type=windowType)
spectrum = essentia.Spectrum(size=len(beat_audio))
beat_spectrum = spectrum(window(beat_audio))
beat_spectral_energy = energy(beat_spectrum)
pool.add(namespace + '.' + 'beats_loudness',
beat_spectral_energy) #, beat_scope)
beats_spectral_energy += beat_spectral_energy
beat_spectral_energybandratio = energybandratio(beat_spectrum)
pool.add(namespace + '.' + 'beats_loudness_bass',
beat_spectral_energybandratio) #, beat_scope)
# filling between-beats arrays
between_beats_end.append(beat_start / float(sampleRate))
between_beats_start.append(beat_end / float(sampleRate))
# display of progress report
progress.update(n_beats / float(sampleRate))
n_beats += 1
between_beats_end.append(duration)
between_beats_spectral_energy = 0.0
# love in between beats
'''
for between_beat_start, between_beat_end in zip(between_beats_start, between_beats_end):
between_beat_audio = audio[between_beat_start * sampleRate : between_beat_end * sampleRate]
between_beat_scope = [between_beat_start, between_beat_end]
window = essentia.Windowing(windowSize = len(between_beat_audio), zeroPadding = 0, type = "blackmanharris62")
spectrum = essentia.Spectrum(size = len(between_beat_audio))
between_beat_spectrum = spectrum(window(between_beat_audio))
between_beat_spectral_energy = energy(between_beat_spectrum)
between_beats_spectral_energy += between_beat_spectral_energy
'''
progress.finish()