def computeNoveltyCurve(filename, pool):
loader = EasyLoader(filename=filename,
sampleRate=pool['samplerate'],
startTime=STARTTIME, endTime=ENDTIME,
downmix=pool['downmix'])
fc = FrameCutter(frameSize=int(pool['framesize']),
silentFrames ='noise',
hopSize=int(pool['hopsize']),
startFromZero=False)
window = Windowing(type=pool['window'],
zeroPhase=False)
#freqBands = FrequencyBands(frequencyBands=EqBands, sampleRate=pool['samplerate'])
freqBands = FrequencyBands(sampleRate=pool['samplerate'])
spec = Spectrum()
hfc = HFC()
loader.audio >> fc.signal
fc.frame >> window.frame >> spec.frame
spec.spectrum >> freqBands.spectrum
spec.spectrum >> hfc.spectrum
freqBands.bands >> (pool, 'frequency_bands')
hfc.hfc >> (pool, 'hfc')
essentia.run(loader)
pool.set('size', loader.audio.totalProduced())
pool.set('length', pool['size']/pool['samplerate'])
# compute a weighting curve that is according to frequency bands:
frequencyBands = pool['frequency_bands']
nFrames = len(frequencyBands)
weightCurve= np.sum(frequencyBands, axis=0)
weightCurve = [val/float(nFrames) for val in weightCurve]
weightCurve = essentia.normalize(weightCurve)
#pyplot.plot(weightCurve)
#pyplot.show()
noveltyCurve = std.NoveltyCurve(frameRate=pool['framerate'],
weightCurveType=pool['weight'],
weightCurve=weightCurve,
normalize=False)(frequencyBands)
#for x in noveltyCurve: pool.add('novelty_curve', x)
#return
# derivative of hfc seems to help in finding more precise beats...
hfc = std.MovingAverage(size=int(0.1*pool['framerate']))(pool['hfc'])
hfc = normalize(hfc)
noveltyCurve = normalize(noveltyCurve)
#noveltyCurve = essentia.normalize(noveltyCurve)
dhfc = derivative(hfc)
print max(hfc), max(noveltyCurve)
for i, val in enumerate(dhfc):
if val< 0: continue
noveltyCurve[i] += 0.1*val
# low pass filter novelty curve:
env = std.Envelope(attackTime=0.001*pool['framerate'],
releaseTime=0.001*pool['framerate'])(noveltyCurve)
# apply median filter:
windowSize = 60./560.*pool['framerate'] #samples
size = len(env)
filtered = zeros(size, dtype='f4')
for i in range(size):
start = i-windowSize
if start < 0: start = 0
end = start + windowSize
if end > size:
end = size
start = size-windowSize
window = env[start:end]
filtered[i] = env[i] - np.median(window) #max(np.median(window), np.mean(window))
if filtered[i] < 0: filtered[i] = 0
#pyplot.subplot(311)
#pyplot.plot(noveltyCurve)
#pyplot.subplot(312)
#pyplot.plot(env, 'r')
#pyplot.subplot(313)
#pyplot.plot(filtered, 'g')
#pyplot.show()
#for x in noveltyCurve: pool.add('novelty_curve', x)
#for x in filtered: pool.add('novelty_curve', x)
#filtered = normalize(filtered)
pool.set('novelty_curve', filtered)
pool.set('original_novelty_curve', noveltyCurve)
def computeNoveltyCurve(filename, pool):
loader = EasyLoader(filename=filename,
sampleRate=pool['samplerate'],
startTime=STARTTIME,
endTime=ENDTIME,
downmix=pool['downmix'])
fc = FrameCutter(frameSize=int(pool['framesize']),
silentFrames='noise',
hopSize=int(pool['hopsize']),
startFromZero=False)
window = Windowing(type=pool['window'], zeroPhase=False)
#freqBands = FrequencyBands(frequencyBands=EqBands, sampleRate=pool['samplerate'])
freqBands = FrequencyBands(sampleRate=pool['samplerate'])
spec = Spectrum()
hfc = HFC()
loader.audio >> fc.signal
fc.frame >> window.frame >> spec.frame
spec.spectrum >> freqBands.spectrum
spec.spectrum >> hfc.spectrum
freqBands.bands >> (pool, 'frequency_bands')
hfc.hfc >> (pool, 'hfc')
essentia.run(loader)
pool.set('size', loader.audio.totalProduced())
pool.set('length', pool['size'] / pool['samplerate'])
# compute a weighting curve that is according to frequency bands:
frequencyBands = pool['frequency_bands']
nFrames = len(frequencyBands)
weightCurve = np.sum(frequencyBands, axis=0)
weightCurve = [val / float(nFrames) for val in weightCurve]
weightCurve = essentia.normalize(weightCurve)
#pyplot.plot(weightCurve)
#pyplot.show()
noveltyCurve = std.NoveltyCurve(frameRate=pool['framerate'],
weightCurveType=pool['weight'],
weightCurve=weightCurve)(frequencyBands)
#for x in noveltyCurve: pool.add('novelty_curve', x)
#return
# derivative of hfc seems to help in finding more precise beats...
hfc = essentia.normalize(pool['hfc'])
dhfc = essentia.derivative(hfc)
for i, val in enumerate(dhfc):
if val < 0: continue
noveltyCurve[i] += val
# low pass filter novelty curve:
env = std.Envelope(attackTime=2. / pool['framerate'],
releaseTime=2. / pool['framerate'])(noveltyCurve)
# apply median filter:
windowSize = 8 #samples
size = len(env)
filtered = zeros(size)
for i in range(size):
start = i - windowSize
if start < 0: start = 0
end = start + windowSize
if end > size:
end = size
start = size - windowSize
filtered[i] = env[i] - np.median(env[start:end])
if filtered[i] < 0: filtered[i] = 0
#pyplot.subplot(311)
#pyplot.plot(noveltyCurve)
#pyplot.subplot(312)
#pyplot.plot(env, 'r')
#pyplot.subplot(313)
#pyplot.plot(filtered, 'g')
#pyplot.show()
#for x in noveltyCurve: pool.add('novelty_curve', x)
for x in filtered:
pool.add('novelty_curve', x)