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)
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))
