def test(f_discretization=10, e_discretization=30, indep=False, selection=None, subset=None, corpus=None, smoothing=None, sensitivity=5, segmentation='reasonable'):
if not selection:
selection = (db.select())
score = db.getScore1(selection)
if not corpus:
(f, e, m) = tools.chooseFeatures()
else:
(f, e, m) = tools.loadFeatures(corpus)
if m['version'] != sf.version:
print("Scorefeatures versions don't match! Corpus version: {0} Scorefeatures version: {1}".format(m['version'], sf.version))
exit(0)
if not subset:
# Select a subset by hand
subset = selectSubset(m['featureset'])
print('\n\tPerforming {0}'.format(selection))
print('\tFeatures version: {0}'.format(m['version']))
print('\tFeatureset used: [', end=' ')
print(', '.join([m['featureset'][i] for i in subset]), end=' ')
print(']')
print('\tScorefeatures discretization: {0}\n\tExpression discretization: {1}'.format(f_discretization, e_discretization))
print('\tSensitivity: {0}'.format(sensitivity))
print('\tSmoothing: {0}'.format(smoothing))
print('\tCorpus: {0}\n'.format(corpus))
if indep:
hmm = HMM_indep(2, smoothing)
else:
hmm = HMM(2, smoothing)
trainHMM(hmm, f, e, f_discretization, e_discretization, subset=subset, ignore=selection, sensitivity=sensitivity)
#trainHMM(hmm, f, e, f_discretization, e_discretization, subset=subset)
hmm.storeInfo('hmm2.txt')
print("Loading score")
melodyscore = Score(score).melody()
melody = tools.parseScore(melodyscore)
# Segmentate the the score
print("Analysing score")
if segmentation == 'reasonable':
print('Using reasonable segmentation')
onset = structure.reasonableSegmentation(melody)
elif segmentation == 'new':
print('Using new segmentation')
onset = structure.newSegmentation(melody)
elif segmentation == 'notelevel':
print('Using notelevel segmentation')
onset = structure.noteLevel(melody)
#onset = structure.groupings(structure.list_to_tree(structure.first_order_tree(structure.onset, melody, 0.1)), 1)
#namelist = []
#for group in onset:
# namelist.append([leaf.name() for leaf in group])
#print namelist
(p, expression) = render(melodyscore, onset, hmm, f_discretization, e_discretization, subset, sensitivity=sensitivity)
print("Done, resulting expression(with a probability of {1}): {0}".format(expression, p))
performance = perform.perform(score, melodyscore, onset, expression, converter=melody)
playPerformance(selection, performance, expression, onset, '{0}_{1}_on_{2}_discret_{3}-{4}_smoothing_{5}'.format(\
selection[0], selection[1], corpus, f_discretization, e_discretization, smoothing))
def loadperformance():
(selection, expression) = tools.loadPerformance()
score = db.getScore1(selection)
melodyscore = Score(score).melody()
melody = tools.parseScore(melodyscore)
segmentation = structure.reasonableSegmentation(melody)
performance = perform.perform(score, Score(score).melody(), segmentation, expression, converter=melody)
seq = Sequencer()
seq.play(performance)
def plotCorpus():
(f, e, m) = tools.chooseFeatures()
d = input('Discretization? ')
if not d == '':
s = input('Sensitivity? [5]')
if s == '':
s = 5.0
d = float(d)
s = float(s)
else: d = None
for work in f:
print('Plotting and saving {0}'.format(work))
expression = e[work]
if d:
expression = [undiscretize_expression(discretize_expression(x, d, sensitivity=s), d, sensitivity=s) for x in expression]
score = db.getScore1(work)
segmentation = structure.reasonableSegmentation(tools.parseScore(Score(score).melody()))
visualize(segmentation, expression, [0, 1, 2], '{0}-{1}-{2}'.format(work[0], work[1], work[2]), '{0}-{1} by {2}'.format(work[0], work[1], work[2]))
def train(trainset):
expression = {}
features = {}
const = 0
Max = 0
Min = None
count = 0
print(">>> Loading scores and deviations, this will take hours and may eat all you memory")
for query in trainset:
print(">>> Loading: {0}".format(query))
score = db.getScore1(query)
deviations = db.getDeviation1(query)
alignment = Alignment(score, deviations)
melody = alignment.melody()
#segments = structure.newSegmentation(tools.parseScore(melody))
segments = structure.noteLevel(tools.parseScore(melody))
const += len(segments)
lengths = sum([len(s) for s in segments])
m = max([len(s) for s in segments])
mi = min([len(s) for s in segments])
if m > Max:
Max = m
if not Min:
Min = mi
if mi < Min:
Min = mi
print('>>> Extracting features')
expression[query] = performancefeatures.vanDerWeijExpression(alignment, segments)
features[query] = scorefeatures.vanDerWeijFeatures(melody, segments)
count += 1
print('{0}/{1} done'.format(count, len(trainset)))
print("Done, {0} segments found with an average length of: {1} (min: {2} max: {3})".format(const, lengths / float(const), Min, Max))
tools.saveFeatures(features, expression)
set = train.trainset(composers, pianist)
train.train(set)
exit(0)
elif a[1] == 'align':
import train
set = [db.select()]
train.train(set)
exit(0)
elif a[1] == 'plotcorpus':
plotCorpus()
exit(0)
elif a[1] == 'plot':
(selection, expression) = tools.loadPerformance()
if selection == None:
selection = db.select()
score = db.getScore1(selection)
melodyscore = Score(score).melody()
melody = tools.parseScore(melodyscore)
segmentation = structure.reasonableSegmentation(melody)
visualize(segmentation, expression)
exit(0)
elif a[1] == 'corpora':
for x in tools.datasets():
print('Name:\t[{0}]\tInfo:\t{1}'.format(x, tools.corpusInfo(x)))
exit(0)
elif a[1] == 'corpusinfo':
choice = util.menu("Select corpus", tools.datasets())
print(tools.corpusInfo(tools.datasets()[choice]))
tools.extendedCorpusInfo(tools.datasets()[choice])
exit(0)
elif a[1] == 'features':
return [[n] for n in notes]
if __name__ == '__main__':
import sys
if len(sys.argv) > 1:
l = [int(x) for x in sys.argv[1:]]
print(relative_deltalist(test, l))
print(second_order_tree(test, l, 0.0, deltalist_function=relative_deltalist))
print(second_order_tree(test, l, 0.0))
sys.exit(0)
import database as db
import tools
w = db.select()
score = Score(db.getScore1(w))
melodyscore = score.melody()
#melodyscore.show()
melody = tools.parseScore(melodyscore, list(range(1, 9)))
trees = [second_order_tree(onset, melody, 0.5), second_order_tree(pitch, melody, 0.0, ), first_order_tree(onset, melody, 0.0), first_order_tree(pitch, melody)]
for tree in trees:
print("Tree")
print(tools.recursive_print(tree))
for i in range(5):
for j in range(len(trees)):
groups = groupings(list_to_tree(trees[j]), i)
avg_group = 0
for group in groups:
import database as db from alignment import * from representation import * from sequencer import * from score import * import tools selection = db.select() score = db.getScore1(selection) notes = tools.parseScore(score) seq = Sequencer() seq.play(notes) #s = Score(alignment.score, alignment)
import database as db from sequencer import * from alignment import * import structure, tools import scorefeatures as sf import performancefeatures as pf import perform s = db.select() a = Alignment(db.getScore1(s), db.getDeviation1(s)) melodyscore = a.melody() melody = tools.parseScore(melodyscore) onset = structure.groupings(structure.list_to_tree(structure.first_order_tree(structure.onset, melody, 0.1)), 1) score = sf.vanDerWeijFeatures(melodyscore, onset) performance = pf.vanDerWeijExpression(a, onset) print(score) print(performance) seq = Sequencer() seq.play(perform.vanDerWeijPerformSimple(a.score, melodyscore, onset, performance, bpm=a.deviations.bpm, converter=melody))
def square(l):
result = []
for i in l:
result.append(i*i)
return result
if len(sys.argv) > 1:
notes = sys.argv[1]
deltas = [int(x) for x in sys.argv[2]]
s3 = structure.second_order_deltarule(notes, deltas, 0)
print(tools.recursive_print(s3))
sys.exit(0)
selection = db.select()
score = Score(db.getScore1(selection).stripTies())
melody = score.melody()
notes = tools.parseScore(melody)
deltas = []
print("COMBINED DELTA TREES")
#for feature in [structure.pitch, structure.onset]:
# deltas.append(normalize(structure.repetition(feature, notes)))
for feature in [structure.onset, structure.duration, structure.pitch]:
print(">>>> {0} absolute:".format(feature))
print(structure.absolute_deltalist(feature, notes))
print(">>>> {0} relative:".format(feature))
print(structure.relative_deltalist(feature, notes))
import util, tools, structure
# Load all the scores and notelists
#while True:
# print db.select()
print(">>>> Loading materials.....")
schumann = ('Schumann', 'kdz007', 'ashke', 'GPO-Concert-Steinway-ver2.sf2')
mozart1 = ('Mozart', 'snt331-3', 'nakam', 'GPO-Concert-Steinway-ver2.sf2')
chopin1 = ('Chopin', 'wlz010', 'ashke', 'Bosendorfer PIANO / GIGA')
mozart2 = ('Mozart', 'snt331-1', 'mo', None)
lastig = ('Bach', 'wtc219-f', 'richt', 'GPO-Concert-Steinway-ver2.sf2')
score = Score(db.getScore1(chopin1).stripTies())
melody = score.melody()
chopinnotes = tools.parseScore(melody)
delta = structure.absolute_deltalist(structure.onset, chopinnotes)
sodelta1 = structure.normalize(structure.second_order_deltalist(delta))
score = Score(db.getScore1(mozart2).stripTies())
melody = score.melody()
mozartnotes = tools.parseScore(melody)
delta2 = structure.square(structure.absolute_deltalist(structure.pitch, mozartnotes))
sodelta2 = structure.normalize(structure.second_order_deltalist(delta2))
s1 = structure.second_order_deltarule(chopinnotes, sodelta1, 0.1)
s2 = structure.second_order_deltarule(mozartnotes, sodelta2, 0.1)
while True:
