def computeSpectrum(signal):
#gen = VectorInput(signal)
#fc = FrameCutter(startFromZero=False, frameSize=48, hopSize=1)
#w = Windowing(zeroPhase=False)
#spec = Spectrum()
#p = essentia.Pool()
#gen.data >> fc.signal
#fc.frame >> w.frame >> spec.frame
#spec.spectrum >> (p,'spectrum')
#essentia.run(gen)
#pyplot.imshow(p['spectrum'], cmap=pyplot.cm.hot, aspect='auto', origin='lower')
corr = std.AutoCorrelation()(signal)
pyplot.plot(corr)
pyplot.show()
print argmax(corr[2:]) + 2
import essentia.standard as es import numpy as np import scipy.signal FS = 44100 w = es.Windowing(type='hann') spectrum = es.Spectrum() centroid = es.Centroid() moments = es.CentralMoments() # Temporal descriptors power = es.InstantPower() log_attack_time = es.LogAttackTime() effective_duration = es.EffectiveDuration() auto_correlation = es.AutoCorrelation() zero_crossing_rate = es.ZeroCrossingRate() # Spectral descriptors peak_freq = es.MaxMagFreq() roll_off = es.RollOff() flux = es.Flux() flatness = es.Flatness() # Harmonic descriptors pitch = es.PitchYin(frameSize=1024) spectral_peaks = es.SpectralPeaks(minFrequency=1e-5) harmonic_peaks = es.HarmonicPeaks() inharmonicity = es.Inharmonicity() oer = es.OddToEvenHarmonicEnergyRatio() tristimulus = es.Tristimulus()
import essentia.standard as ess
# matplotlib without any blocking GUI
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from smst.utils import audio
(fs, x) = audio.read_wav('../../../sounds/piano.wav')
start = 13860
M = 800
xp = x[start:start + M] / float(max(x[start:start + M]))
r = ess.AutoCorrelation(normalization='standard')(xp)
r = r / max(r)
peaks = ess.PeakDetection(threshold=.11, interpolate=False, minPosition=.01)(r)
plt.figure(1, figsize=(9, 7))
plt.subplot(211)
plt.plot(np.arange(M) / float(fs), xp, lw=1.5)
plt.axis([0, (M - 1) / float(fs), min(xp), max(xp)])
plt.xlabel('time (sec)')
plt.ylabel('amplitude')
plt.title('x (piano.wav)')
plt.subplot(212)
plt.plot(np.arange(M) / float(fs), r, 'r', lw=1.5)
plt.plot(peaks[0] * (M - 1) / float(fs),
peaks[1],
'x',
def plot(pool, title, outputfile='out.svg', subplot=111):
''' plots bars for each beat'''
#computeSpectrum(pool['loudness'])
ticks = pool['ticks']
#barSize = min([ticks[i+1] - ticks[i] for i in range(len(ticks[:-1]))])/2.
barSize = 0.8
offset = barSize / 2.
loudness = pool['loudness']
loudnessBand = pool['loudnessBandRatio'] # ticks x bands
medianRatiosPerTick = []
meanRatiosPerTick = []
for tick, energy in enumerate(loudnessBand):
medianRatiosPerTick.append(median(energy))
meanRatiosPerTick.append(mean(energy))
loudnessBand = copy.deepcopy(loudnessBand.transpose()) # bands x ticks
#xcorr = std.CrossCorrelation(minLag=0, maxLag=16)
#acorr = std.AutoCorrelation()
#bandCorr = []
#for iBand, band in enumerate(loudnessBand):
# bandCorr.append(acorr(essentia.array(band)))
nBands = len(loudnessBand)
nticks = len(loudness)
maxRatiosPerBand = []
medianRatiosPerBand = []
meanRatiosPerBand = []
for idxBand, band in enumerate(loudnessBand):
maxRatiosPerBand.append([0] * nticks)
medianRatiosPerBand.append([0] * nticks)
meanRatiosPerBand.append([0] * nticks)
for idxTick in range(nticks):
start = idxTick
end = start + BEATWINDOW
if (end > nticks):
howmuch = end - nticks
end = nticks - 1
start = end - howmuch
if start < 0: start = 0
medianRatiosPerBand[idxBand][idxTick] = median(band[start:end])
maxRatiosPerBand[idxBand][idxTick] = max(band[start:end])
meanRatiosPerBand[idxBand][idxTick] = mean(band[start:end])
for iBand, band in enumerate(loudnessBand):
for tick, ratio in enumerate(band):
#if ratio < medianRatiosPerBand[iBand][tick] and\
# ratio <= medianRatiosPerTick[tick]: loudnessBand[iBand][tick]=0
bandThreshold = max(medianRatiosPerBand[iBand][tick],
meanRatiosPerBand[iBand][tick])
tickThreshold = max(medianRatiosPerTick[tick],
meanRatiosPerTick[tick])
if ratio < bandThreshold and ratio <= tickThreshold:
loudnessBand[iBand][tick] = 0
else:
loudnessBand[iBand][tick] *= loudness[tick]
#if loudnessBand[iBand][tick] > 1 : loudnessBand[iBand][tick] = 1
acorr = std.AutoCorrelation()
bandCorr = []
maxCorr = []
for iBand, band in enumerate(loudnessBand):
bandCorr.append(acorr(essentia.array(band)))
maxCorr.append(argmax(bandCorr[-1][2:]) + 2)
# use as much window space as possible:
pyplot.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.95)
pyplot.subplot(511)
pyplot.imshow(bandCorr,
cmap=pyplot.cm.hot,
aspect='auto',
origin='lower',
interpolation='nearest')
print 'max correlation', maxCorr
sumCorr = []
for tick in range(nticks):
total = 0
for band in bandCorr:
total += band[tick]
sumCorr.append(total)
sumCorr[0] = 0
sumCorr[1] = 0
pyplot.subplot(512)
maxAlpha = max(sumCorr)
for i, val in enumerate(sumCorr):
alpha = max(0, min(val / maxAlpha, 1))
pyplot.bar(i,
1,
barSize,
align='edge',
bottom=0,
alpha=alpha,
color='r',
edgecolor='w',
linewidth=.3)
print 'max sum correlation', argmax(sumCorr[2:]) + 2
hist = getHarmonics(sumCorr)
maxHist = argmax(hist)
print 'max histogram', maxHist
#for idx,val in enumerate(hist):
# if val < maxHist: hist[idx] = 0
pyplot.subplot(513)
for i, val in enumerate(hist):
pyplot.bar(i,
val,
barSize,
align='edge',
bottom=0,
color='r',
edgecolor='w',
linewidth=.3)
peakDetect = std.PeakDetection(maxPeaks=5,
orderBy='amplitude',
minPosition=0,
maxPosition=len(sumCorr) - 1,
range=len(sumCorr) - 1)
peaks = peakDetect(sumCorr)[0]
peaks = [round(x + 1e-15) for x in peaks]
print 'Peaks:', peaks
pyplot.subplot(514)
maxAlpha = max(sumCorr)
for i, val in enumerate(sumCorr):
alpha = max(0, min(val / maxAlpha, 1))
pyplot.bar(i,
val,
barSize,
align='edge',
bottom=0,
alpha=alpha,
color='r',
edgecolor='w',
linewidth=.3)
# multiply both histogram and sum corr to have a weighted histogram:
wHist = essentia.array(hist) * sumCorr * acorr(loudness)
maxHist = argmax(wHist)
print 'max weighted histogram', maxHist
pyplot.subplot(515)
maxAlpha = max(wHist)
for i, val in enumerate(wHist):
alpha = max(0, min(val / maxAlpha, 1))
pyplot.bar(i,
val,
barSize,
align='edge',
bottom=0,
alpha=alpha,
color='r',
edgecolor='w',
linewidth=.3)
pyplot.savefig(outputfile, dpi=300)
#pyplot.show()
return
