def audio_to_chroma(input_wavfile, hopsize, fx, norm=0):
"""Method for turning a wavefile into chroma features.
Parameters
----------
input_wavfile : str
Path to a wavefile.
hopsize : int
Number of samples between frames.
fx : function
Function that consumes 2D matrices of DFT coefficients and outputs
chroma features.
norm : scalar, default=0
Lp norm to apply to the features; skipped if not > 0.
Returns
-------
features : np.ndarray
Matrix of time-aligned chroma vectors, shaped (num_frames, 12).
"""
sigbuff = signal_buffer(input_wavfile, hopsize=hopsize)
pitch_spec = np.concatenate([CT.cqt_pool(batch)
for batch in sigbuff], axis=0)
features = fx(pitch_spec)
if norm > 0:
features = CT.lp_norm(features, norm)
return features
def prepare_training_data(train_file, label_file, label_map, batch_size=100):
"""Create a data generator from input data and label files.
Parameters
----------
train_file : str
Path to a numpy file of data observations.
label_file : str
Path to a numpy file of data labels.
label_map : dict
Dictionary mapping string labels to integers.
batch_size : int, default=100
Number of datapoints to return for each batch.
Returns
-------
shuffler : generator
Data generator that returns an (x,y) tuple for each call
to next().
stats : dict
Coefficient means and standard deviations, keyed by 'mu' and 'sigma'.
"""
data, labels = np.load(train_file), np.load(label_file)
y_true = np.array([label_map.get(l, -1) for l in labels])
valid_idx = y_true > 0
# Drop all labels that don't exist in the label map, i.e. negative.
data, y_true = data[valid_idx], y_true[valid_idx]
data = CT.cqt_pool(data)
# Compute standardization statistics.
stats = {'mu': data.mean(axis=0), 'sigma': data.std(axis=0)}
num_qualities = int(y_true.max() / 12)
templates = generate_chroma_templates(num_qualities)
return data_shuffler(data, templates[y_true], batch_size=batch_size), stats
def prepare_training_data(train_file, label_file, label_map, batch_size=100):
"""Create a data generator from input data and label files.
Parameters
----------
train_file : str
Path to a numpy file of data observations.
label_file : str
Path to a numpy file of data labels.
label_map : dict
Dictionary mapping string labels to integers.
batch_size : int, default=100
Number of datapoints to return for each batch.
Returns
-------
shuffler : generator
Data generator that returns an (x,y) tuple for each call
to next().
stats : dict
Coefficient means and standard deviations, keyed by 'mu' and 'sigma'.
"""
data, labels = np.load(train_file), np.load(label_file)
y_true = np.array([label_map.get(l, -1) for l in labels])
valid_idx = y_true > 0
# Drop all labels that don't exist in the label map, i.e. negative.
data, y_true = data[valid_idx], y_true[valid_idx]
data = CT.cqt_pool(data)
# Compute standardization statistics.
stats = {'mu': data.mean(axis=0), 'sigma': data.std(axis=0)}
num_qualities = int(y_true.max() / 12)
templates = generate_chroma_templates(num_qualities)
return data_shuffler(data, templates[y_true], batch_size=batch_size), stats
def audio_to_chroma(input_wavfile, hopsize, fx, norm=0):
"""Method for turning a wavefile into chroma features.
Parameters
----------
input_wavfile : str
Path to a wavefile.
hopsize : int
Number of samples between frames.
fx : function
Function that consumes 2D matrices of DFT coefficients and outputs
chroma features.
norm : scalar, default=0
Lp norm to apply to the features; skipped if not > 0.
Returns
-------
features : np.ndarray
Matrix of time-aligned chroma vectors, shaped (num_frames, 12).
"""
sigbuff = signal_buffer(input_wavfile, hopsize=hopsize)
pitch_spec = np.concatenate([CT.cqt_pool(batch) for batch in sigbuff],
axis=0)
features = fx(pitch_spec)
if norm > 0:
features = CT.lp_norm(features, norm)
return features
