def evaluate_clustering(x, clusters):
""" Returns the root mean square of the differences between array points and their closest clusters. """
x = _np.asarray(x)
error = 0
# Can be heavily optimized, but let's assume there are few clusters.
for point in x:
c = _mh.find_nearest_value(clusters, point)
error += (point - c) ** 2
return _np.sqrt(error / x.size)
def evaluate_clustering(x, clusters):
""" Returns the root mean square of the differences between array points and their closest clusters. """
x = _np.asarray(x)
error = 0
# Can be heavily optimized, but let's assume there are few clusters.
for point in x:
c = _mh.find_nearest_value(clusters, point)
error += (point - c)**2
return _np.sqrt(error / x.size)
def plot_tracking(audiopath,
title="",
binsize=1470,
tune=False,
plotpath=None,
repetitions=10):
""" Plots the HPS tracking of an audio file. """
samplerate, samples = _sf.readfile(audiopath)
detections = samples.size // binsize
p = _np.zeros(repetitions * detections)
for i in range(detections):
f = _hps.hps(samples[i * binsize:(i + 1) * binsize])
if tune:
f = _mh.find_nearest_value(_mt.notes, f)
p = _np.repeat(p, repetitions)
_pl.plot(p)
_pl.title(title)
xlocs = _np.linspace(0, 10 * detections, 5)
_pl.xlabel("Time (s)")
_pl.xlim([0, _np.max(xlocs)])
_pl.xticks(xlocs, [
"%.2f" % l
for l in _np.multiply(xlocs, binsize / (repetitions * samplerate))
])
_pl.ylabel("Fundamental Frequency (Hz)")
_pl.ylim((0.9 * _np.min(p), 1.05 * _np.max(p)))
if plotpath:
_pl.savefig(plotpath, bbox_inches="tight")
else:
_pl.show()
_pl.clf()
def finalize(self):
""" Finalizes the transcriber. Will close resources, perform calculations and normalizations based on the whole
transcription (e.g.: normalize note duration) then writes the transcription to the desired outputs. """
self.close()
# Extract the last note.
if self.current_ticks > 2:
self.notes.append({
"name":
self.current_note,
"duration":
np.log2(self.current_ticks),
"ticks":
self.current_ticks,
"slur":
"stop" if self.currently_slurring else False
})
if DEBUG_NOTE:
print("%s\t %d\t %.3fs" %
(self.current_note, self.current_ticks,
self.current_ticks / self.blocks_per_sec))
print("\n\n###### Detected notes:")
for note in self.notes:
print(note)
durations = np.array([n["duration"] for n in self.notes])
clusters = clst.equidistant_clusterize(durations)
corrected_notes = [{
"name":
n["name"],
"duration":
2**mh.find_nearest_value(clusters, n["duration"]),
"slur":
n["slur"]
} for n in self.notes]
print("\n\n###### Corrected notes:")
for note in corrected_notes:
print(note)
most_common = scipy.stats.mode(
[n["duration"] for n in corrected_notes])[0][0]
tempo = int(round(60 * self.blocks_per_sec / most_common, 0))
while tempo < 80:
tempo *= 2
most_common /= 2
while tempo > 220:
tempo /= 2
most_common *= 2
if WRITE_MIDI or WRITE_XML:
s = music21.stream.Stream()
s.append(music21.tempo.MetronomeMark(number=tempo))
s.append(music21.meter.TimeSignature('4/4'))
for note in corrected_notes:
n = music21.note.Note()
n.pitch.name = note["name"]
n.duration.quarterLength = note["duration"] / most_common
s.append(n)
note["music21"] = n
slurring = False
slur = music21.spanner.Slur()
for note in corrected_notes:
if note["slur"] == "start":
slurring = True
if slurring:
slur.addSpannedElements([note["music21"]])
if note["slur"] == "stop":
slurring = False
s.insert(0, slur)
slur = music21.spanner.Slur()
if slurring:
s.insert(0, slur)
s.insert(0, s.analyze('key'))
if WRITE_MIDI:
sf.write_m21stream_to_midi(s, MIDI_FILENAME)
if WRITE_XML:
s.show('musicxml')
if WRITE_OUT:
print("### Writing processed output file")
f = open(OUT_FILENAME, 'w')
f.write(self.out)
f.flush()
os.fsync(f)
return
def update(self):
""" Performs a transcription iteration update, i.e. wait for microphone data to be available,
then update the transcriber state (detect pitch, note duration, etc). """
if not self.tong:
raise AssertionError(
"Please initialize the tonguing detector first. (missing a call to detect_noise()?)"
)
self.total_ticks += 1
new_samples = self.mic.listen()
if DEBUG_PERF:
rms_start_time = time.time()
rms = np.sqrt(np.mean(np.square(new_samples)))
if DEBUG_PERF:
self.rms_time = time.time() - rms_start_time
tong_start_time = time.time()
# Feed the new samples to the Tonguing Detector.
# Beware we shouldn't send repeated samples, so we send the new_samples and not the entire block.
tongued = self.tong.feed(new_samples)
if DEBUG_PERF:
self.tong_time = time.time() - tong_start_time
if tongued:
if self.current_ticks > 2:
# We detected tonguing, so split the current note.
# TODO: if 'previous_note' is considered noisy, account for it in the duration.
# TODO: Consider whether we should increment the current tick partially (proportionally to the audible portion?).
self.notes.append({
"name":
self.current_note,
"duration":
np.log2(self.current_ticks),
"ticks":
self.current_ticks,
"slur":
"stop" if self.currently_slurring else False
})
self.currently_slurring = False
if DEBUG_NOTE:
print("%s\t %d\t %.3fs" %
(self.current_note, self.current_ticks,
self.current_ticks / self.blocks_per_sec))
if DEBUG_TONG:
print("TONG")
if WRITE_OUT:
self.out += "%d\t: TONG\n" % self.total_ticks
self.current_ticks = 0
# Add the new_samples at the beginning of the block, so they replace the oldest values.
self.block[0:self.samples_per_read] = new_samples
# Now roll the block back so that it is in chronological order.
# Not strictly necessary as we're discarding phase, but it makes replacing old values easier and also ensures
# usual windowing will smooth discontinuities at the borders.
self.block = np.roll(self.block, -self.samples_per_read)
# No need to proceed if we're to discard the pitch due to insufficient RMS power in the block.
if not DEBUG_NOISE and rms < self.noise_threshold:
self.out += "%d\t: 'rms < self.noise_threshold'\n" % self.total_ticks
return
# We want pitch, so pass the block to the PDA
if DEBUG_PERF:
hps_start_time = time.time()
perceived_f = pda.hps.hps(self.block,
fs=self.rate,
harmonics=3,
precision=2)
if DEBUG_PERF:
self.hps_time = time.time() - hps_start_time
# and tune the pitch down to a known note.
tuned_f = mh.find_nearest_value(mt.notes, perceived_f)
note = mt.note_name[tuned_f]
# TODO: rough error percentage estimate
error = perceived_f - tuned_f
percentage = np.sign(error) * 2 * error / (tuned_f * (1 + mt.semitone)
if error > 0 else tuned_f *
(1 - mt.semitone))
if note == self.current_note:
if self.current_note == self.previous_note:
# We're receiving a new sample of the current note.
self.current_ticks += 1
else:
# Our 'previous_note' measurement was probably noisy.
# Pretend it was a measurement of 'current_note', and account ticks for both.
self.current_ticks += 2
elif note == self.previous_note:
# Keep in mind that all notes are 'tentative' until their tick count is > n, so:
# - C5 C5 C5 D5 D5 means we successfully identified a C5 and the beginning of a D5, assuming n is 1.
if self.current_ticks > 2:
self.notes.append({
"name":
self.current_note,
"duration":
np.log2(self.current_ticks),
"ticks":
self.current_ticks,
"slur":
"continue" if self.currently_slurring else "start"
})
self.currently_slurring = True
if DEBUG_NOTE:
print("%s\t %d\t %.3fs" %
(self.current_note, self.current_ticks,
self.current_ticks / self.blocks_per_sec))
self.current_note = note
self.current_ticks = 2
elif self.previous_note != self.current_note:
# Experimentally, it's pretty rare to have 2 noisy detections in a row, so if we find 2 different measurements
# we can assume the old note has ended.
# - C5 C5 C5 D5 E5 means we identified a C5 end, but we don't know the next note yet.
if self.current_ticks > 2:
self.notes.append({
"name":
self.current_note,
"duration":
np.log2(self.current_ticks),
"ticks":
self.current_ticks,
"slur":
"continue" if self.currently_slurring else False
})
if DEBUG_NOTE:
print("%s\t %d\t %.3fs" %
(self.current_note, self.current_ticks,
self.current_ticks / self.blocks_per_sec))
# We currently have no idea of the note being played, so assign an error string to it.
# When we have k identical detections in a row (with k defined in the elifs above) we will successfully
# assign the current note.
self.current_note = "NOISE_ERR"
self.current_ticks = 0
self.previous_note = note
if DEBUG_TICK:
print("%s\t (%.3f)\t@ %.2f" % (note, percentage, rms))
if WRITE_OUT:
self.out += "%d\t: %s\t (%.3f)\t@ %.2f\r\n" % (
self.total_ticks, note, percentage, rms)
return
def finalize(self):
""" Finalizes the transcriber. Will close resources, perform calculations and normalizations based on the whole
transcription (e.g.: normalize note duration) then writes the transcription to the desired outputs. """
self.close()
# Extract the last note.
if self.current_ticks > 2:
self.notes.append({"name": self.current_note,
"duration": np.log2(self.current_ticks),
"ticks": self.current_ticks,
"slur": "stop" if self.currently_slurring else False})
if DEBUG_NOTE:
print("%s\t %d\t %.3fs"%(self.current_note, self.current_ticks, self.current_ticks/self.blocks_per_sec))
print("\n\n###### Detected notes:")
for note in self.notes:
print(note)
durations = np.array([n["duration"] for n in self.notes])
clusters = clst.equidistant_clusterize(durations)
corrected_notes = [{"name": n["name"],
"duration": 2**mh.find_nearest_value(clusters, n["duration"]),
"slur": n["slur"]}
for n in self.notes]
print("\n\n###### Corrected notes:")
for note in corrected_notes:
print(note)
most_common = scipy.stats.mode([n["duration"] for n in corrected_notes])[0][0]
tempo = int(round(60*self.blocks_per_sec/most_common, 0))
while tempo < 80:
tempo *= 2
most_common /= 2
while tempo > 220:
tempo /= 2
most_common *= 2
if WRITE_MIDI or WRITE_XML:
s = music21.stream.Stream()
s.append(music21.tempo.MetronomeMark(number=tempo))
s.append(music21.meter.TimeSignature('4/4'))
for note in corrected_notes:
n = music21.note.Note()
n.pitch.name = note["name"]
n.duration.quarterLength = note["duration"]/most_common
s.append(n)
note["music21"] = n
slurring = False
slur = music21.spanner.Slur()
for note in corrected_notes:
if note["slur"] == "start":
slurring = True
if slurring:
slur.addSpannedElements([note["music21"]])
if note["slur"] == "stop":
slurring = False
s.insert(0, slur)
slur = music21.spanner.Slur()
if slurring:
s.insert(0, slur)
s.insert(0, s.analyze('key'))
if WRITE_MIDI:
sf.write_m21stream_to_midi(s, MIDI_FILENAME)
if WRITE_XML:
s.show('musicxml')
if WRITE_OUT:
print("### Writing processed output file")
f = open(OUT_FILENAME, 'w')
f.write(self.out)
f.flush()
os.fsync(f)
return
def update(self):
""" Performs a transcription iteration update, i.e. wait for microphone data to be available,
then update the transcriber state (detect pitch, note duration, etc). """
if not self.tong:
raise AssertionError("Please initialize the tonguing detector first. (missing a call to detect_noise()?)")
self.total_ticks += 1
new_samples = self.mic.listen()
if DEBUG_PERF:
rms_start_time = time.time()
rms = np.sqrt(np.mean(np.square(new_samples)))
if DEBUG_PERF:
self.rms_time = time.time() - rms_start_time
tong_start_time = time.time()
# Feed the new samples to the Tonguing Detector.
# Beware we shouldn't send repeated samples, so we send the new_samples and not the entire block.
tongued = self.tong.feed(new_samples)
if DEBUG_PERF:
self.tong_time = time.time() - tong_start_time
if tongued:
if self.current_ticks > 2:
# We detected tonguing, so split the current note.
# TODO: if 'previous_note' is considered noisy, account for it in the duration.
# TODO: Consider whether we should increment the current tick partially (proportionally to the audible portion?).
self.notes.append({"name": self.current_note,
"duration": np.log2(self.current_ticks),
"ticks": self.current_ticks,
"slur": "stop" if self.currently_slurring else False})
self.currently_slurring = False
if DEBUG_NOTE:
print("%s\t %d\t %.3fs"%(self.current_note, self.current_ticks, self.current_ticks/self.blocks_per_sec))
if DEBUG_TONG:
print("TONG")
if WRITE_OUT:
self.out += "%d\t: TONG\n" % self.total_ticks
self.current_ticks = 0
# Add the new_samples at the beginning of the block, so they replace the oldest values.
self.block[0:self.samples_per_read] = new_samples
# Now roll the block back so that it is in chronological order.
# Not strictly necessary as we're discarding phase, but it makes replacing old values easier and also ensures
# usual windowing will smooth discontinuities at the borders.
self.block = np.roll(self.block, -self.samples_per_read)
# No need to proceed if we're to discard the pitch due to insufficient RMS power in the block.
if not DEBUG_NOISE and rms < self.noise_threshold:
self.out += "%d\t: 'rms < self.noise_threshold'\n" % self.total_ticks
return;
# We want pitch, so pass the block to the PDA
if DEBUG_PERF:
hps_start_time = time.time()
perceived_f = pda.hps.hps(self.block, fs=self.rate, harmonics=3, precision=2)
if DEBUG_PERF:
self.hps_time = time.time() - hps_start_time
# and tune the pitch down to a known note.
tuned_f = mh.find_nearest_value(mt.notes, perceived_f)
note = mt.note_name[tuned_f]
# TODO: rough error percentage estimate
error = perceived_f - tuned_f
percentage = np.sign(error) * 2 * error/(tuned_f*(1 + mt.semitone) if error > 0 else tuned_f*(1 - mt.semitone))
if note == self.current_note:
if self.current_note == self.previous_note:
# We're receiving a new sample of the current note.
self.current_ticks += 1
else:
# Our 'previous_note' measurement was probably noisy.
# Pretend it was a measurement of 'current_note', and account ticks for both.
self.current_ticks += 2
elif note == self.previous_note:
# Keep in mind that all notes are 'tentative' until their tick count is > n, so:
# - C5 C5 C5 D5 D5 means we successfully identified a C5 and the beginning of a D5, assuming n is 1.
if self.current_ticks > 2:
self.notes.append({"name": self.current_note,
"duration": np.log2(self.current_ticks),
"ticks": self.current_ticks,
"slur": "continue" if self.currently_slurring else "start"})
self.currently_slurring = True
if DEBUG_NOTE:
print("%s\t %d\t %.3fs"%(self.current_note, self.current_ticks, self.current_ticks/self.blocks_per_sec))
self.current_note = note
self.current_ticks = 2
elif self.previous_note != self.current_note:
# Experimentally, it's pretty rare to have 2 noisy detections in a row, so if we find 2 different measurements
# we can assume the old note has ended.
# - C5 C5 C5 D5 E5 means we identified a C5 end, but we don't know the next note yet.
if self.current_ticks > 2:
self.notes.append({"name": self.current_note,
"duration": np.log2(self.current_ticks),
"ticks": self.current_ticks,
"slur": "continue" if self.currently_slurring else False})
if DEBUG_NOTE:
print("%s\t %d\t %.3fs"%(self.current_note, self.current_ticks, self.current_ticks/self.blocks_per_sec))
# We currently have no idea of the note being played, so assign an error string to it.
# When we have k identical detections in a row (with k defined in the elifs above) we will successfully
# assign the current note.
self.current_note = "NOISE_ERR"
self.current_ticks = 0
self.previous_note = note
if DEBUG_TICK:
print("%s\t (%.3f)\t@ %.2f" % (note, percentage, rms))
if WRITE_OUT:
self.out += "%d\t: %s\t (%.3f)\t@ %.2f\r\n" % (self.total_ticks, note, percentage, rms)
return