def _stable_pitches_to_notes(self, stable_pitches_hz,
theoretical_intervals, tonic_hz):
stable_pitches_cent = Converter.hz_to_cent(stable_pitches_hz, tonic_hz)
# Finding nearest theoretical values of each stable pitch, identify the
# name of this value and write to output
stable_notes = {} # Defining output (return) object
for stable_pitch_cent, stable_pitch_hz in zip(stable_pitches_cent,
stable_pitches_hz):
note_cent = TonicLastNote.find_nearest(
theoretical_intervals.values(), stable_pitch_cent)
if abs(stable_pitch_cent - note_cent) < self.pitch_threshold:
for key, val in theoretical_intervals.iteritems():
if val == note_cent:
theoretical_pitch = Converter.cent_to_hz(
note_cent, tonic_hz)
stable_notes[key] = {
"performed_interval": {"value": stable_pitch_cent,
"unit": "cent"},
"theoretical_interval": {"value": note_cent,
"unit": "cent"},
"theoretical_pitch": {"value": theoretical_pitch,
"unit": "cent"},
"stable_pitch": {"value": stable_pitch_hz,
"unit": "Hz"}}
break
return stable_notes
def check_tonic_with_octave_correction(self, tonic, distribution):
# shift the distribution to tonic
distribution.bins -= Converter.hz_to_cent(tonic, distribution.ref_freq)
distribution.ref_freq = tonic
# get the stable pitches
peaks = distribution.detect_peaks()
peak_idx = peaks[0]
stable_pitches = distribution.bins[peak_idx]
# find all the frequencies in the tonic candidate's pitch class
pitches_in_tonic_pitch_class = [
sp for sp in stable_pitches
if min([sp % 1200, 1200 - (sp % 1200)]) < self.stable_pitch_dev]
# sum all the pitch occurrences in the pitch distribution starting from
# these pitches till their octave
pitch_weights = []
for pp in pitches_in_tonic_pitch_class:
vals_in_octave = distribution.vals[(pp <= distribution.bins) *
(distribution.bins < pp + 1200)]
pitch_weights.append(np.sum(vals_in_octave))
# the candidate which accumulates the highest weight is the tonic
try:
tonic_corr_cent = pitches_in_tonic_pitch_class[
pitch_weights.index(max(pitch_weights))]
return Converter.cent_to_hz(tonic_corr_cent, tonic)
except ValueError:
return None # no stable pitch class found for the given frequency
def _slice_pitch(self, pp, ti, tt):
p_sliced = [p for t, p in zip(tt, pp) if ti[1] > t >= ti[0]]
p_cent = Converter.hz_to_cent(p_sliced, self._dummy_ref_freq,
min_freq=20.0)
# pop nan and inf
p_cent = p_cent[~np.isnan(p_cent)]
p_cent = p_cent[~np.isinf(p_cent)] # shouldn't exist, but anyways...
return p_cent, p_sliced
def _get_tunings(newtonic, note_models):
for nm in note_models.values():
interval = Converter.hz_to_cent(nm['stable_pitch']['Value'],
newtonic['alignment']['Value'])
nm['performed_interval'] = {'Value': interval, 'Unit': 'cent'}
theo_pitch = Converter.cent_to_hz(
nm['theoretical_interval']['Value'],
newtonic['alignment']['Value'])
nm['theoretical_pitch'] = {'Value': theo_pitch, 'Unit': 'Hz'}
def _parse_pitch_input(pitch_in, tonic_freq):
"""
Parses the pitch input from list, numpy array or file.
If the input (or the file content) is a matrix, the method assumes the
columns represent timestamps, pitch and "other columns".
respectively. It only returns the second column in this case.
:param pitch_in: pitch input, which is a list, numpy array or filename
:param tonic_freq: the tonic frequency in Hz
:return: parsed pitch track (numpy array)
"""
# parse the pitch track from txt file, list or numpy array
try:
p = np.loadtxt(pitch_in)
except ValueError:
logger.debug('pitch_in is not a filename')
p = np.array(pitch_in)
p = p[:, 1] if p.ndim > 1 else p # get the pitch stream
# normalize wrt tonic
return Converter.hz_to_cent(p, tonic_freq)
def _parse_pitch_input(pitch_in, tonic_freq):
"""
Parses the pitch input from list, numpy array or file.
If the input (or the file content) is a matrix, the method assumes the
columns represent timestamps, pitch and "other columns".
respectively. It only returns the second column in this case.
:param pitch_in: pitch input, which is a list, numpy array or filename
:param tonic_freq: the tonic frequency in Hz
:return: parsed pitch track (numpy array)
"""
# parse the pitch track from txt file, list or numpy array
try:
p = np.loadtxt(pitch_in)
except ValueError:
logger.debug('pitch_in is not a filename')
p = np.array(pitch_in)
p = p[:, 1] if p.ndim > 1 else p # get the pitch stream
# normalize wrt tonic
return Converter.hz_to_cent(p, tonic_freq)
