def audioVirelaiSearch():
#from music21 import audioSearch
from music21.audioSearch import transcriber
from music21 import search
virelaisSheet = cadencebook.TrecentoSheet(sheetname='virelais')
virelaiCantuses = []
for i in range(2, 54):
thisVirelai = virelaisSheet.makeWork(i)
if thisVirelai.title != "":
try:
vc = thisVirelai.incipit.getElementsByClass('Part')[0]
vc.insert(0, metadata.Metadata(title=thisVirelai.title))
virelaiCantuses.append(vc)
except IndexError:
pass
searchScore = transcriber.runTranscribe(show=False,
plot=False,
seconds=10.0,
saveFile=False)
#from music21 import converter
#searchScore = converter.parse("c'4 a8 a4 g8 b4. d'4. c8 b a g f4", '6/8')
#searchScore.show()
l = search.approximateNoteSearch(searchScore, virelaiCantuses)
for i in l:
print(i.metadata.title, i.matchProbability)
l[0].show()
def recognizeScore(scorePart, pageMeasureNumbers, iterations=1):
pages = []
for i in range(len(pageMeasureNumbers) - 1):
pages.append(
scorePart.measures(pageMeasureNumbers[i],
pageMeasureNumbers[i + 1]).flat.notes.stream())
#divide into 24 note groups, overlapping by 16 notes each...
allStreams = []
for pgMinus1 in range(len(pages)):
print(str(pgMinus1 + 1))
thisPage = pages[pgMinus1]
totalNotes = len(thisPage)
for i in range(0, totalNotes, 8):
startNote = thisPage[i]
startMeasure = startNote.measureNumber
print(" " + str(startMeasure))
newStream = stream.Stream(thisPage[i:i + 24])
newStream.pageNumber = pgMinus1 + 1
newStream.startMeasure = startMeasure
allStreams.append(newStream)
for loopy in range(iterations):
if loopy > 0:
print("\n\nstarting again in 3 seconds")
time.sleep(3)
searchScore = audioSearch.transcriber.runTranscribe(show=False,
plot=False,
seconds=15.0,
saveFile=False)
l = search.approximateNoteSearch(searchScore, allStreams)
scores = [0 for j in range(len(pages))]
for i in range(8): # top 8 searches
topStream = l[i]
scorePage = topStream.pageNumber - 1
scores[scorePage] += (topStream.matchProbability / (i + 1.5)) * 10
print("\nBest guesses (pg#, starting measure, probability)")
for i, st in enumerate(l):
print(st.pageNumber, st.startMeasure, st.matchProbability)
if i >= 7:
break
print("\nWeighed top scores (pg#, score):")
indexOfMaxScore = argmax_index(scores)
for i in range(len(pages)):
print((i + 1, scores[i]), end="")
if i == indexOfMaxScore:
print(" **** ")
else:
print("")
def recognizeScore(scorePart, pageMeasureNumbers, iterations=1):
pages = []
for i in range(len(pageMeasureNumbers) - 1):
pages.append(scorePart.measures(pageMeasureNumbers[i],
pageMeasureNumbers[i + 1]).flat.notes.stream())
#divide into 24 note groups, overlapping by 16 notes each...
allStreams = []
for pgMinus1 in range(len(pages)):
print(str(pgMinus1 + 1))
thisPage = pages[pgMinus1]
totalNotes = len(thisPage)
for i in range(0, totalNotes, 8):
startNote = thisPage[i]
startMeasure = startNote.measureNumber
print(" " + str(startMeasure))
newStream = stream.Stream(thisPage[i:i + 24])
newStream.pageNumber = pgMinus1 + 1
newStream.startMeasure = startMeasure
allStreams.append(newStream)
for loopy in range(iterations):
if loopy > 0:
print("\n\nstarting again in 3 seconds")
time.sleep(3)
searchScore = audioSearch.transcriber.runTranscribe(show=False, plot=False,
seconds=15.0, saveFile=False)
l = search.approximateNoteSearch(searchScore, allStreams)
scores = [0 for j in range(len(pages))]
for i in range(8): # top 8 searches
topStream = l[i]
scorePage = topStream.pageNumber - 1
scores[scorePage] += (topStream.matchProbability / (i + 1.5))*10
print("\nBest guesses (pg#, starting measure, probability)")
for i,st in enumerate(l):
print(st.pageNumber, st.startMeasure, st.matchProbability)
if i >= 7:
break
print("\nWeighed top scores (pg#, score):")
indexOfMaxScore = argmax_index(scores)
for i in range(len(pages)):
print( (i + 1, scores[i]), end="")
if i == indexOfMaxScore:
print(" **** ")
else:
print("")
def audioVirelaiSearch():
#from music21 import audioSearch
from music21.audioSearch import transcriber
from music21 import search
virelaisSheet = cadencebook.TrecentoSheet(sheetname = 'virelais')
virelaiCantuses = []
for i in range(2, 54):
thisVirelai = virelaisSheet.makeWork(i)
if thisVirelai.title != "":
try:
vc = thisVirelai.incipit.getElementsByClass('Part')[0]
vc.insert(0, metadata.Metadata(title = thisVirelai.title))
virelaiCantuses.append(vc)
except IndexError:
pass
searchScore = transcriber.runTranscribe(show = False, plot = False, seconds = 10.0, saveFile = False)
#from music21 import converter
#searchScore = converter.parse("c'4 a8 a4 g8 b4. d'4. c8 b a g f4", '6/8')
#searchScore.show()
l = search.approximateNoteSearch(searchScore, virelaiCantuses)
for i in l:
print(i.metadata.title, i.matchProbability)
l[0].show()
def matchingNotes(
self,
scoreStream,
transcribedScore,
notePrediction,
lastNotePosition,
):
from music21 import audioSearch
# Analyzing streams
tn_recording = int(len(transcribedScore.flat.notesAndRests))
totScores = []
beginningData = []
lengthData = []
END_OF_SCORE = False
# take 10% more of samples
tn_window = int(math.ceil(tn_recording * 1.1))
hop = int(math.ceil(tn_window / 4))
if hop == 0:
iterations = 1
else:
iterations = int((math.floor(len(scoreStream) / hop)) -
math.ceil(tn_window / hop))
for i in range(iterations):
scNotes = scoreStream[i * hop + 1:i * hop + tn_recording + 1]
name = '%d' % i
beginningData.append(i * hop + 1)
lengthData.append(tn_recording)
scNotes.id = name
totScores.append(scNotes)
listOfParts = search.approximateNoteSearchWeighted(
transcribedScore.flat.notesAndRests.stream(), totScores)
#decision process
if notePrediction > len(scoreStream) - tn_recording - hop - 1:
notePrediction = len(scoreStream) - tn_recording - hop - 1
END_OF_SCORE = True
environLocal.printDebug('LAST PART OF THE SCORE')
#lastCountdown = self.countdown
position, self.countdown = audioSearch.decisionProcess(
listOfParts,
notePrediction,
beginningData,
lastNotePosition,
self.countdown,
self.firstNotePage,
self.lastNotePage,
)
totalLength = 0
number = int(listOfParts[position].id)
if self.silencePeriod is True and self.silencePeriodCounter < 5:
# print(lastCountdown, self.countdown, lastNotePosition,
# beginningData[number], lengthData[number])
environLocal.printDebug('All rest period')
self.countdown -= 1
if self.countdown != 0:
probabilityHit = 0
else:
probabilityHit = listOfParts[position].matchProbability
unused_listOfParts2 = search.approximateNoteSearch(
transcribedScore.flat.notesAndRests.stream(), totScores)
unused_listOfParts3 = search.approximateNoteSearchNoRhythm(
transcribedScore.flat.notesAndRests.stream(), totScores)
unused_listOfParts4 = search.approximateNoteSearchOnlyRhythm(
transcribedScore.flat.notesAndRests.stream(), totScores)
# print('PROBABILITIES:',)
# print('pitches and durations weighted (current)', listOfParts[position].matchProbability,)
# print('pitches and durations without weighting' , listOfParts2[position].matchProbability,)
# print('pitches', listOfParts3[position].matchProbability,)
# print('durations', listOfParts4[position].matchProbability)
for i in range(len(totScores[number])):
totalLength = totalLength + totScores[number][i].quarterLength
if self.countdown == 0 and self.silencePeriodCounter == 0:
lastNotePosition = beginningData[number] + lengthData[number]
return totalLength, lastNotePosition, probabilityHit, END_OF_SCORE
def matchingNotes(
self,
scoreStream,
transcribedScore,
notePrediction,
lastNotePosition,
):
from music21 import audioSearch
# Analyzing streams
tn_recording = int(len(transcribedScore.flat.notesAndRests))
totScores = []
beginningData = []
lengthData = []
END_OF_SCORE = False
# take 10% more of samples
tn_window = int(math.ceil(tn_recording * 1.1))
hop = int(math.ceil(tn_window / 4))
if hop == 0:
iterations = 1
else:
iterations = int((math.floor(len(scoreStream) / hop)) -
math.ceil(tn_window / hop))
for i in range(iterations):
scNotes = scoreStream[i * hop + 1:i * hop + tn_recording + 1]
name = "%d" % i
beginningData.append(i * hop + 1)
lengthData.append(tn_recording)
scNotes.id = name
totScores.append(scNotes)
listOfParts = search.approximateNoteSearchWeighted(
transcribedScore.flat.notesAndRests.stream(), totScores)
#decision process
if notePrediction > len(scoreStream) - tn_recording - hop - 1:
notePrediction = len(scoreStream) - tn_recording - hop - 1
END_OF_SCORE = True
environLocal.printDebug("LAST PART OF THE SCORE")
#lastCountdown = self.countdown
position, self.countdown = audioSearch.decisionProcess(
listOfParts,
notePrediction,
beginningData,
lastNotePosition,
self.countdown,
self.firstNotePage,
self.lastNotePage,
)
totalLength = 0
number = int(listOfParts[position].id)
if self.silencePeriod is True and self.silencePeriodCounter < 5:
# print(lastCountdown, self.countdown, lastNotePosition,
# beginningData[number], lengthData[number])
environLocal.printDebug("All rest period")
self.countdown -= 1
if self.countdown != 0:
probabilityHit = 0
else:
probabilityHit = listOfParts[position].matchProbability
unused_listOfParts2 = search.approximateNoteSearch(
transcribedScore.flat.notesAndRests.stream(), totScores)
unused_listOfParts3 = search.approximateNoteSearchNoRhythm(
transcribedScore.flat.notesAndRests.stream(), totScores)
unused_listOfParts4 = search.approximateNoteSearchOnlyRhythm(
transcribedScore.flat.notesAndRests.stream(), totScores)
# print "PROBABILITIES:",
# print "pitches and durations weighted (current)",listOfParts[position].matchProbability,
# print "pitches and durations without weighting" , listOfParts2[position].matchProbability,
# print "pitches", listOfParts3[position].matchProbability,
# print "durations",listOfParts4[position].matchProbability
for i in range(len(totScores[number])):
totalLength = totalLength + totScores[number][i].quarterLength
if self.countdown == 0 and self.silencePeriodCounter == 0:
lastNotePosition = beginningData[number] + lengthData[number]
return totalLength, lastNotePosition, probabilityHit, END_OF_SCORE
