def _preprocess(self, valid_features=["hpcp", "tonnetz", "mfcc", "cqt", "tempogram"],
normalize=True):
"""This method obtains the actual features."""
# Read features
if self.features is None:
self.features = io.get_features(self.audio_file,
annot_beats=self.annot_beats,
framesync=self.framesync)
# Use specific feature
if self.feature_str not in valid_features:
raise RuntimeError("Feature %s in not valid for algorithm: %s "
"(valid features are %s)." %
(self.feature_str, __name__, valid_features))
else:
try:
F = self.features[self.feature_str]
except KeyError:
raise RuntimeError("Feature %s in not supported by MSAF" %
(self.feature_str))
# Normalize if needed
if normalize:
F = U.lognormalize_chroma(F)
return F
def _preprocess(self,
valid_features=["hpcp", "tonnetz", "mfcc", "cqt"],
normalize=True):
"""This method obtains the actual features."""
# Read features
if self.features is None:
self.features = io.get_features(self.audio_file,
annot_beats=self.annot_beats,
framesync=self.framesync)
# Use specific feature
if self.feature_str not in valid_features:
raise RuntimeError("Feature %s in not valid for algorithm: %s "
"(valid features are %s)." %
(self.feature_str, __name__, valid_features))
else:
try:
F = self.features[self.feature_str]
except KeyError:
raise RuntimeError("Feature %s in not supported by MSAF" %
(self.feature_str))
# Normalize if needed
if normalize:
F = U.lognormalize_chroma(F)
return F
def _preprocess(self,
valid_features=["hpcp", "tonnetz", "mfcc", "cqt", "gmt"],
normalize=True):
"""This method obtains the actual features."""
# Read features
self.hpcp, self.mfcc, self.tonnetz, self.cqt, self.gmt, beats, dur, self.anal = \
io.get_features(self.audio_file, annot_beats=self.annot_beats,
framesync=self.framesync,
pre_features=self.features)
# Use specific feature
if self.feature_str not in valid_features:
raise RuntimeError("Feature %s in not valid for algorithm: %s "
"(valid features are %s)." %
(self.feature_str, __name__, valid_features))
else:
try:
F = eval("self." + self.feature_str)
except:
raise RuntimeError("Feature %s in not supported by MSAF" %
(self.feature_str))
# Normalize if needed
if normalize:
F = U.lognormalize_chroma(F)
return F
def _preprocess(self, valid_features=["hpcp", "tonnetz", "mfcc", "cqt"],
normalize=True):
"""This method obtains the actual features."""
# Read features
self.hpcp, self.mfcc, self.tonnetz, self.cqt, beats, dur, self.anal = \
io.get_features(self.audio_file, annot_beats=self.annot_beats,
framesync=self.framesync,
pre_features=self.features)
# Use specific feature
if self.feature_str not in valid_features:
raise RuntimeError("Feature %s in not valid for algorithm: %s "
"(valid features are %s)." %
(self.feature_str, __name__, valid_features))
else:
try:
F = eval("self." + self.feature_str)
except:
raise RuntimeError("Feature %s in not supported by MSAF" %
(self.feature_str))
# Normalize if needed
if normalize:
F = U.lognormalize_chroma(F)
return F
def _preprocess(self,
valid_features=["hpcp", "tonnetz", "mfcc"],
normalize=True):
"""This method obtains the actual features, their frame times,
and the boundary indeces in these features if needed."""
# Read features
if self.features is None:
# Features stored in a json file
self.hpcp, self.mfcc, self.tonnetz, beats, dur, anal = \
io.get_features(self.audio_file, annot_beats=self.annot_beats,
framesync=self.framesync)
else:
# Features passed as parameters
feat_prefix = ""
if not self.framesync:
feat_prefix = "bs_"
self.hpcp = self.features["%shpcp" % feat_prefix]
self.mfcc = self.features["%smfcc" % feat_prefix]
self.tonnetz = self.features["%stonnetz" % feat_prefix]
beats = self.features["beats"]
dur = self.features["anal"]["dur"]
anal = self.features["anal"]
# Store analysis parameters
self.anal = anal
# Use correct frames to find times
frame_times = beats
if self.framesync:
frame_times = U.get_time_frames(dur, anal)
# Read input bounds if necessary
bound_idxs = None
if self.in_bound_times is not None:
bound_idxs = io.align_times(self.in_bound_times, frame_times)
bound_idxs = np.unique(bound_idxs)
# Use specific feature
if self.feature_str not in valid_features:
raise RuntimeError("Feature %s in not valid for algorithm: %s "
"(valid features are %s)." %
(self.feature_str, __name__, valid_features))
else:
try:
F = eval("self." + self.feature_str)
except:
raise RuntimeError("Feature %s in not supported by MSAF" %
(self.feature_str))
# Normalize if needed
if normalize:
F = U.lognormalize_chroma(F)
return F, frame_times, dur, bound_idxs
def _preprocess(
self,
valid_features=["pcp", "tonnetz", "mfcc", "cqt", "tempogram"],
normalize=True):
"""This method obtains the actual features."""
# Use specific feature
if self.feature_str not in valid_features:
raise RuntimeError("Feature %s in not valid for algorithm: %s "
"(valid features are %s)." %
(self.feature_str, __name__, valid_features))
else:
try:
F = self.features.features
except KeyError:
raise RuntimeError("Feature %s in not supported by MSAF" %
(self.feature_str))
# Normalize if needed
if normalize:
F = U.lognormalize_chroma(F)
return F
