def getModels(pitch, alignednotes, tonic, kernel_width=2.5):
noteNames = set(an['Symbol'] for an in alignednotes)
noteModels = dict((nn, {'notes':[], 'distribution':[],
'stablepitch':[]}) for nn in noteNames)
# get the complete histogram
noteModels['all'] = {'distribution':getModelDistribution(pitch[:,1]),
'notes':None, 'stablepitch': None}
# compute note trajectories and add to each model
for an in alignednotes:
if not an['Interval'][0] == an['Interval'][1]: # not aligned
an['trajectory'] = np.vstack(p for p in pitch
if an['Interval'][0] <= p[0] <= an['Interval'][1])
noteModels[an['Symbol']]['notes'].append(an)
# compute the histogram for each model
for key in noteModels.keys():
if not key == 'all':
tempPitchVals = np.hstack(nn['trajectory'][:,1]
for nn in noteModels[key]['notes'])
noteModels[key]['distribution']=getModelDistribution(tempPitchVals)
# get the stable pitch
theoreticalpeak = noteModels[key]['notes'][0]['Pitch']['Value']
peakCandIdx = noteModels[key]['distribution'].detect_peaks()[0]
peakCandFreqs = [noteModels[key]['distribution'].bins[i] for i in peakCandIdx]
peakCandCents = PitchDistribution.hz_to_cent(peakCandFreqs, tonic)
minId = abs(peakCandCents - theoreticalpeak).argmin()
noteModels[key]['stablepitch'] = peakCandFreqs[minId]
# scale according to relative usage of each note
stablepitchVal = noteModels[key]['distribution'].vals[peakCandIdx[minId]]
allhistbin_id = abs(PitchDistribution.hz_to_cent(
noteModels['all']['distribution'].bins,peakCandFreqs[minId])).argmin()
allhistval = noteModels['all']['distribution'].vals[allhistbin_id]
noteModels[key]['distribution'].vals = (noteModels[key]['distribution'].vals
* allhistval / stablepitchVal)
return noteModels
def getModelDistribution(pitchVals, kernel_width=2.5): dummyFreq = 440.0 step_size = 7.5 tempCentVals = PitchDistribution.hz_to_cent(pitchVals, dummyFreq) distribution = PitchDistribution.generate_pd(tempCentVals, ref_freq=dummyFreq, kernel_width=kernel_width, step_size=step_size) distribution.bins = PitchDistribution.cent_to_hz(distribution.bins, dummyFreq) return distribution
