def __init__(self, file_struct, feat_type, sr=config.sample_rate,
hop_length=config.hop_size, mode=config.cqt.mode,
norm=config.cqt.norm, epsilon=config.cqt.epsilon,
ref_power=config.cqt.ref_power):
"""Constructor of the class.
Parameters
----------
file_struct: `msaf.input_output.FileStruct`
Object containing the file paths from where to extract/read
the features.
feat_type: `FeatureTypes`
Enum containing the type of features.
sr: int > 0
Sampling rate for the analysis.
hop_length: int > 0
Hop size in frames for the analysis.
mode: {'stack', 'merge'}
Mode for flattening multi-track piano-rolls.
norm: bool
True to normalize the piano-rolls. False to do nothing.
epsilon: float
A small number added to the results to avoid divide by zero errors.
ref_power: function
The reference power for logarithmic scaling.
"""
# Init the parent
super().__init__(file_struct=file_struct, sr=sr, hop_length=hop_length,
feat_type=feat_type)
# Init the CQT parameters
if mode not in ('stack', 'merge'):
raise FeatureParamsError("Wrong value for mode")
self.mode = mode
self.norm = norm
self.epsilon = epsilon
if ref_power == "max":
self.ref_power = np.max
elif ref_power == "min":
self.ref_power = np.min
elif ref_power == "median":
self.ref_power = np.median
else:
raise FeatureParamsError("Wrong value for ref_power")
def __init__(self,
file_struct,
feat_type,
sr=config.sample_rate,
hop_length=config.hop_size,
n_fft=config.n_fft,
n_mels=config.mfcc.n_mels,
n_mfcc=config.mfcc.n_mfcc,
ref_power=config.mfcc.ref_power,
n_bins=config.cqt.bins,
norm=config.cqt.norm,
filter_scale=config.cqt.filter_scale):
"""Constructor of the class.
Parameters
----------
file_struct: `msaf.input_output.FileStruct`
Object containing the file paths from where to extract/read
the features.
feat_type: `FeatureTypes`
Enum containing the type of features.
sr: int > 0
Sampling rate for the analysis.
hop_length: int > 0
Hop size in frames for the analysis.
n_fft: int > 0
Number of frames for the FFT.
n_mels: int > 0
Number of mel filters.
n_mfcc: int > 0
Number of mel coefficients.
ref_power: function
The reference power for logarithmic scaling.
"""
# Init the parent
super().__init__(file_struct=file_struct,
sr=sr,
hop_length=hop_length,
feat_type=feat_type)
# Init the MFCC parameters
self.n_fft = n_fft
self.n_mels = n_mels
self.n_mfcc = n_mfcc
# Init the CQT parameters
self.n_bins = n_bins
self.norm = norm
self.filter_scale = filter_scale
if ref_power == "max":
self.ref_power = np.max
elif ref_power == "min":
self.ref_power = np.min
elif ref_power == "median":
self.ref_power = np.median
else:
raise FeatureParamsError("Wrong value for ref_power")
def __init__(self,
file_struct,
feat_type,
sr=config.sample_rate,
hop_length=config.hop_size,
n_bins=config.cqt.bins,
norm=config.cqt.norm,
filter_scale=config.cqt.filter_scale,
ref_power=config.cqt.ref_power):
"""Constructor of the class.
Parameters
----------
file_struct: `msaf.input_output.FileStruct`
Object containing the file paths from where to extract/read
the features.
feat_type: `FeatureTypes`
Enum containing the type of features.
sr: int > 0
Sampling rate for the analysis.
hop_length: int > 0
Hop size in frames for the analysis.
n_bins: int > 0
Number of frequency bins for the CQT.
norm: float
Type of norm to use for basis function normalization.
filter_scale: float
The scale of the filter for the CQT.
ref_power: str
The reference power for logarithmic scaling.
See `configdefaults.py` for the possible values.
"""
# Init the parent
super().__init__(file_struct=file_struct,
sr=sr,
hop_length=hop_length,
feat_type=feat_type)
# Init the CQT parameters
self.n_bins = n_bins
self.norm = norm
self.filter_scale = filter_scale
if ref_power == "max":
self.ref_power = np.max
elif ref_power == "min":
self.ref_power = np.min
elif ref_power == "median":
self.ref_power = np.median
else:
raise FeatureParamsError("Wrong value for ref_power")
def read_features(self, tol=1e-3):
"""Reads the features from a file and stores them in the current
object.
Parameters
----------
tol: float
Tolerance level to detect duration of audio.
"""
try:
# Read JSON file
with open(self.file_struct.features_file) as f:
feats = json.load(f)
# Store duration
if self.dur is None:
self.dur = float(feats["globals"]["dur"])
# Check that we have the correct global parameters
assert (np.isclose(self.dur,
float(feats["globals"]["dur"]),
rtol=tol))
assert (self.sr == int(feats["globals"]["sample_rate"]))
assert (self.hop_length == int(feats["globals"]["hop_length"]))
assert (os.path.basename(
self.file_struct.audio_file) == os.path.basename(
feats["globals"]["audio_file"]))
# Check for specific features params
feat_params_err = FeatureParamsError(
"Couldn't find features for %s id in file %s" %
(self.get_id(), self.file_struct.features_file))
if self.get_id() not in feats.keys():
raise feat_params_err
for param_name in self.get_param_names():
value = getattr(self, param_name)
if hasattr(value, '__call__'):
# Special case of functions
if value.__name__ != \
feats[self.get_id()]["params"][param_name]:
raise feat_params_err
else:
if str(value) != \
feats[self.get_id()]["params"][param_name]:
raise feat_params_err
# Store actual features
self._est_beats_times = np.array(feats["est_beats"])
self._est_beatsync_times = np.array(feats["est_beatsync_times"])
self._est_beats_frames = librosa.core.time_to_frames(
self._est_beats_times, sr=self.sr, hop_length=self.hop_length)
self._framesync_features = \
np.array(feats[self.get_id()]["framesync"])
self._est_beatsync_features = \
np.array(feats[self.get_id()]["est_beatsync"])
# Read annotated beats if available
if "ann_beats" in feats.keys():
self._ann_beats_times = np.array(feats["ann_beats"])
self._ann_beatsync_times = np.array(
feats["ann_beatsync_times"])
self._ann_beats_frames = librosa.core.time_to_frames(
self._ann_beats_times,
sr=self.sr,
hop_length=self.hop_length)
self._ann_beatsync_features = \
np.array(feats[self.get_id()]["ann_beatsync"])
except KeyError:
raise WrongFeaturesFormatError(
"The features file %s is not correctly formatted" %
self.file_struct.features_file)
except AssertionError:
raise FeaturesNotFound(
"The features for the given parameters were not found in "
"features file %s" % self.file_struct.features_file)
except IOError:
raise NoFeaturesFileError("Could not find features file %s",
self.file_struct.features_file)
