def analHpsModelStreaming(params, signal):
#out = numpy.array(0)
pool = essentia.Pool()
fcut = es.FrameCutter(frameSize=params['frameSize'],
hopSize=params['hopSize'],
startFromZero=False)
w = es.Windowing(type="blackmanharris92")
spec = es.Spectrum(size=params['frameSize'])
# pitch detection
pitchDetect = es.PitchYinFFT(frameSize=params['frameSize'],
sampleRate=params['sampleRate'])
smanal = es.HpsModelAnal(sampleRate=params['sampleRate'],
hopSize=params['hopSize'],
maxnSines=params['maxnSines'],
magnitudeThreshold=params['magnitudeThreshold'],
freqDevOffset=params['freqDevOffset'],
freqDevSlope=params['freqDevSlope'],
minFrequency=params['minFrequency'],
maxFrequency=params['maxFrequency'],
stocf=params['stocf'])
# add half window of zeros to input signal to reach same ooutput length
signal = numpy.append(signal, zeros(params['frameSize'] // 2))
insignal = VectorInput (signal)
# analysis
insignal.data >> fcut.signal
fcut.frame >> w.frame
w.frame >> spec.frame
spec.spectrum >> pitchDetect.spectrum
fcut.frame >> smanal.frame
pitchDetect.pitch >> smanal.pitch
pitchDetect.pitch >> (pool, 'pitch')
pitchDetect.pitchConfidence >> (pool, 'pitchConfidence')
smanal.magnitudes >> (pool, 'magnitudes')
smanal.frequencies >> (pool, 'frequencies')
smanal.phases >> (pool, 'phases')
smanal.stocenv >> (pool, 'stocenv')
essentia.run(insignal)
# remove first half window frames
mags = pool['magnitudes']
freqs = pool['frequencies']
phases = pool['phases']
pitchConf = pool['pitchConfidence']
# remove short tracks
minFrames = int(params['minSineDur'] * params['sampleRate'] / params['hopSize'])
freqsClean = cleaningHarmonicTracks(freqs, minFrames, pitchConf)
pool['frequencies'].data = freqsClean
return mags, freqsClean, phases
def analsynthHarmonicMaskStreaming(params, signal):
out = array([0.])
pool = essentia.Pool()
# windowing and FFT
fcut = es.FrameCutter(frameSize=params['frameSize'],
hopSize=params['hopSize'],
startFromZero=False)
w = es.Windowing(type="blackmanharris92")
fft = es.FFT(size=params['frameSize'])
spec = es.Spectrum(size=params['frameSize'])
# pitch detection
pitchDetect = es.PitchYinFFT(frameSize=params['frameSize'],
sampleRate=params['sampleRate'])
hmask = es.HarmonicMask(sampleRate=params['sampleRate'],
binWidth=params['binWidth'],
attenuation=params['attenuation_dB'])
ifft = es.IFFT(size=params['frameSize'])
overl = es.OverlapAdd(frameSize=params['frameSize'],
hopSize=params['hopSize'])
# add half window of zeros to input signal to reach same ooutput length
signal = numpy.append(signal, zeros(params['frameSize'] // 2))
insignal = VectorInput(signal)
# analysis
insignal.data >> fcut.signal
fcut.frame >> w.frame
w.frame >> spec.frame
w.frame >> fft.frame
spec.spectrum >> pitchDetect.spectrum
fft.fft >> hmask.fft
pitchDetect.pitch >> hmask.pitch
pitchDetect.pitchConfidence >> (pool, 'pitchConfidence')
hmask.fft >> ifft.fft
ifft.frame >> overl.frame
overl.signal >> (pool, 'audio')
essentia.run(insignal)
# remove first half window frames
outaudio = pool['audio']
outaudio = outaudio[2 * params['hopSize']:]
return outaudio, pool
def analsynthHprModelStreaming(params, signal):
out = array([0.])
pool = essentia.Pool()
# windowing and FFT
fcut = es.FrameCutter(frameSize = params['frameSize'], hopSize = params['hopSize'], startFromZero = False);
w = es.Windowing(type = "blackmanharris92");
spec = es.Spectrum(size = params['frameSize']);
# pitch detection
pitchDetect = es.PitchYinFFT(frameSize=params['frameSize'], sampleRate = params['sampleRate'])
smanal = es.HprModelAnal(sampleRate = params['sampleRate'], hopSize = params['hopSize'], maxnSines = params['maxnSines'], magnitudeThreshold = params['magnitudeThreshold'], freqDevOffset = params['freqDevOffset'], freqDevSlope = params['freqDevSlope'], minFrequency = params['minFrequency'], maxFrequency = params['maxFrequency'])
synFFTSize = min(int(params['frameSize']/4), 4*params['hopSize']) # make sure the FFT size is appropriate
smsyn = es.SprModelSynth(sampleRate=params['sampleRate'],
fftSize=synFFTSize,
hopSize=params['hopSize'])
# add half window of zeros to input signal to reach same ooutput length
signal = numpy.append(signal, zeros(params['frameSize'] // 2))
insignal = VectorInput (signal)
# analysis
insignal.data >> fcut.signal
fcut.frame >> w.frame
w.frame >> spec.frame
spec.spectrum >> pitchDetect.spectrum
fcut.frame >> smanal.frame
pitchDetect.pitch >> smanal.pitch
pitchDetect.pitchConfidence >> (pool, 'pitchConfidence')
pitchDetect.pitch >> (pool, 'pitch')
# synthesis
smanal.magnitudes >> smsyn.magnitudes
smanal.frequencies >> smsyn.frequencies
smanal.phases >> smsyn.phases
smanal.res >> smsyn.res
smsyn.frame >> (pool, 'frames')
smsyn.sineframe >> (pool, 'sineframes')
smsyn.resframe >> (pool, 'resframes')
essentia.run(insignal)
outaudio = framesToAudio(pool['frames'])
outaudio = outaudio[2*params['hopSize']:]
return outaudio, pool
def analsynthHarmonicModelStreaming(params, signal):
out = array([0.])
pool = essentia.Pool()
# windowing and FFT
fcut = es.FrameCutter(frameSize=params['frameSize'],
hopSize=params['hopSize'],
startFromZero=False)
w = es.Windowing(type="blackmanharris92")
fft = es.FFT(size=params['frameSize'])
spec = es.Spectrum(size=params['frameSize'])
# pitch detection
pitchDetect = es.PitchYinFFT(frameSize=params['frameSize'],
sampleRate=params['sampleRate'])
smanal = es.HarmonicModelAnal(
sampleRate=params['sampleRate'],
maxnSines=params['maxnSines'],
magnitudeThreshold=params['magnitudeThreshold'],
freqDevOffset=params['freqDevOffset'],
freqDevSlope=params['freqDevSlope'],
minFrequency=params['minFrequency'],
maxFrequency=params['maxFrequency'])
smsyn = es.SineModelSynth(sampleRate=params['sampleRate'],
fftSize=params['frameSize'],
hopSize=params['hopSize'])
ifft = es.IFFT(size=params['frameSize'])
overl = es.OverlapAdd(frameSize=params['frameSize'],
hopSize=params['hopSize'])
# add half window of zeros to input signal to reach same ooutput length
signal = numpy.append(signal, zeros(params['frameSize'] / 2))
insignal = VectorInput(signal)
# analysis
insignal.data >> fcut.signal
fcut.frame >> w.frame
w.frame >> spec.frame
w.frame >> fft.frame
spec.spectrum >> pitchDetect.spectrum
fft.fft >> smanal.fft
pitchDetect.pitch >> smanal.pitch
pitchDetect.pitchConfidence >> (pool, 'pitchConfidence')
smanal.magnitudes >> (pool, 'magnitudes')
smanal.frequencies >> (pool, 'frequencies')
smanal.phases >> (pool, 'phases')
# synthesis
smanal.magnitudes >> smsyn.magnitudes
smanal.frequencies >> smsyn.frequencies
smanal.phases >> smsyn.phases
smsyn.fft >> ifft.fft
ifft.frame >> overl.frame
overl.signal >> (pool, 'audio')
essentia.run(insignal)
# remove short tracks
freqs = pool['frequencies']
minFrames = int(params['minSineDur'] * params['sampleRate'] /
params['hopSize'])
freqsClean = cleaningSineTracks(freqs, minFrames)
pool['frequencies'].data = freqsClean
# remove first half window frames
outaudio = pool['audio']
outaudio = outaudio[2 * params['hopSize']:]
return outaudio, pool
