def score_model(model, x, b, t):
# First, transform the data
if model is not None:
try:
xt = model.dot(x)
except:
return 0.0
else:
xt = x
# Then, run the segmenter
kmin, kmax = segmenter.get_num_segs(b[-1])
boundary_beats = segmenter.get_segments(xt, kmin=kmin, kmax=kmax)
if len(boundary_beats) < 2 or len(t) < 2:
return 0.0
t = np.unique(t)
boundary_times = mir_eval.util.adjust_events(b[boundary_beats],
t_min=0.0,
t_max=t[-1])[0]
truth_intervals = mir_eval.util.boundaries_to_intervals(t)
pred_intervals = mir_eval.util.boundaries_to_intervals(boundary_times)
score = mir_eval.segment.detection(truth_intervals,
pred_intervals,
trim=True)[-1]
return score
def score_model(model, x, b, t):
# First, transform the data
if model is not None:
try:
xt = model.dot(x)
except:
return 0.0
else:
xt = x
# Then, run the segmenter
kmin, kmax = segmenter.get_num_segs(b[-1])
boundary_beats = segmenter.get_segments(xt, kmin=kmin, kmax=kmax)
if len(boundary_beats) < 2 or len(t) < 2:
return 0.0
t = np.unique(t)
boundary_times = mir_eval.util.adjust_events(b[boundary_beats], t_min=0.0, t_max=t[-1])[0]
truth_intervals = mir_eval.util.boundaries_to_intervals(t)[0]
pred_intervals = mir_eval.util.boundaries_to_intervals(boundary_times)[0]
score = mir_eval.segment.detection(truth_intervals, pred_intervals, trim=True)[-1]
return score
def score_model(model, x, b, t):
# First, transform the data
if model is not None:
xt = model.dot(x)
else:
xt = x
# Then, run the segmenter
kmin, kmax = segmenter.get_num_segs(b[-1])
boundary_beats = segmenter.get_segments(xt, kmin=kmin, kmax=kmax)
if len(boundary_beats) < 2 or len(t) < 2:
return 0.0
boundary_times = mir_eval.util.adjust_events(b[boundary_beats],
t_min=0.0,
t_max=t[-1])[0]
# Convert boundaries to intervals
truth_intervals = mir_eval.util.boundaries_to_intervals(t)[0]
pred_intervals = mir_eval.util.boundaries_to_intervals(boundary_times)[0]
score = mir_eval.segment.boundary_detection(truth_intervals,
pred_intervals)[-1]
return score
if __name__ == '__main__':
parameters = process_arguments()
# Load the features
print('- ', os.path.basename(parameters['input_song']))
X, Y, beats = features(parameters['input_song'])
# Load the transformation
W = segmenter.load_transform(parameters['transform'])
print('\tapplying transformation...')
X = W.dot(X)
# Find the segment boundaries
print('\tpredicting segments...')
if parameters['gnostic']:
S = segmenter.get_segments(X, kmin=len(Y) - 1, kmax=len(Y))
elif parameters['dynamic']:
kmin, kmax = get_num_segs(beats[-1])
S = segmenter.get_segments(X, kmin=kmin, kmax=kmax)
else:
S = segmenter.get_segments(X)
# Output lab file
print('\tsaving output to ', parameters['output_file'])
segmenter.save_segments(parameters['output_file'], S, beats)
pass
return vars(parser.parse_args(sys.argv[1:]))
if __name__ == '__main__':
parameters = process_arguments()
# Load the features
print('- ', os.path.basename(parameters['input_song']))
X, Y, beats = features(parameters['input_song'])
# Load the transformation
W = segmenter.load_transform(parameters['transform'])
print('\tapplying transformation...')
X = W.dot(X)
# Find the segment boundaries
print('\tpredicting segments...')
if parameters['gnostic']:
S = segmenter.get_segments(X, kmin=len(Y)-1, kmax=len(Y))
elif parameters['dynamic']:
kmin, kmax = get_num_segs(beats[-1])
S = segmenter.get_segments(X, kmin=kmin, kmax=kmax)
else:
S = segmenter.get_segments(X)
# Output lab file
print('\tsaving output to ', parameters['output_file'])
segmenter.save_segments(parameters['output_file'], S, beats)
pass