for i, p in enumerate(audio_test['pattern']):
for _p in p:
start = _p[0] - audio_test['info'][2][0]
end = _p[1] - audio_test['info'][2][0]
len_list.append(end - start)
ground[i][start:end + 1] = i + 1
min_len = int(
min(len_list) * len(subbeat_mat[ind]) /
(audio_test['info'][2][1] - audio_test['info'][2][0])) + 1
min_len_list.append(min_len)
print min_len_list
start_time = time.time()
for ind in range(5):
chroma_frames = feature_mat[ind].transpose()
r = (0.0, 1.0, 0.01)
ideal_v_inv = vmo.find_threshold(chroma_frames,
r=r,
flag='a',
dfunc='other',
dfunc_handle=trnspose_inv,
VERBOSE=False)
oracle_inv = vmo.build_oracle(chroma_frames,
flag='a',
threshold=ideal_v_inv[0][1],
feature='chroma',
dfunc='other',
dfunc_handle=trnspose_inv)
pattern = van.find_repeated_patterns(oracle_inv, lower=5)
print str(time.time() - start_time)
ground = np.zeros((len(audio_test['pattern']),
audio_test['info'][2][1] - audio_test['info'][2][0]))
for i, p in enumerate(audio_test['pattern']):
for _p in p:
start = _p[0] - audio_test['info'][2][0]
end = _p[1] - audio_test['info'][2][0]
ground[i][start:end + 1] = 1
ground_vec.append(ground)
pttr_vec = []
min_len = int(np.mean(oracle_vec[0].lrs) / 2)
for oracle in oracle_vec:
pttr_vec.append(van.find_repeated_patterns(oracle, lower=min_len))
# pattern_vec[song_list[ind]] = pttr_vec
# pattern_mat_vec[song_list[ind]] = pttr_mat_vec
#
# pttr_vec = pattern_vec[song_list[ind]]
# pttr_mat_vec = pattern_mat_vec[song_list[ind]]
# oracle_vec = vmo_dict[song_list[ind]]
ref_pttr = pttr_vec[0][:]
per_pttr = pttr_vec[1:5][:]
add_pttr_list = []
pttr = per_pttr.pop()
# min_len = int(np.mean(oracle_vec[0].lrs)/2)
while per_pttr != []:
for i, p in enumerate(pttr):
ground = np.zeros((len(audio_test["pattern"]), audio_test["info"][2][1] - audio_test["info"][2][0]))
len_list = []
for i, p in enumerate(audio_test["pattern"]):
for _p in p:
start = _p[0] - audio_test["info"][2][0]
end = _p[1] - audio_test["info"][2][0]
len_list.append(end - start)
ground[i][start : end + 1] = i + 1
# min_len = int(min(len_list)*len(subbeats)/(audio_test['info'][2][1]-audio_test['info'][2][0])/2)+1
# min_len = int(stats.hmean(np.array(oracle_inv.lrs)[np.where(np.array(oracle_inv.lrs) != 0)]))
# min_len = 5
min_len = int(np.mean(oracle_inv.lrs) / 2) # Current optimal setting
# print min_len
### Extract Repeated Suffixes from VMO
pattern = van.find_repeated_patterns(oracle_inv, lower=min_len)
### Beat Time to Time
pattern_time = []
for pttr in pattern:
pttr_time = []
for p in pttr[0]:
time = librosa.core.frames_to_time(
np.array([subbeats[p - pttr[1]], subbeats[p - 1]]), sr=sr, n_fft=fft_size, hop_length=hop_size
)
pttr_time.append(time)
pattern_time.append(pttr_time)
pattern_bpm = []
for pt_time in pattern_time:
subbeats.append(query.shape[1])
ground = np.zeros((len(audio_test['pattern']), audio_test['info'][2][1]-audio_test['info'][2][0]))
for i,p in enumerate(audio_test['pattern']):
for _p in p:
start = _p[0] - audio_test['info'][2][0]
end = _p[1] - audio_test['info'][2][0]
ground[i][start:end+1] = 1
ground_vec.append(ground)
pttr_vec = []
min_len = int(np.mean(oracle_vec[0].lrs)/2)
for oracle in oracle_vec:
pttr_vec.append(van.find_repeated_patterns(oracle, lower = min_len))
# pattern_vec[song_list[ind]] = pttr_vec
# pattern_mat_vec[song_list[ind]] = pttr_mat_vec
#
# pttr_vec = pattern_vec[song_list[ind]]
# pttr_mat_vec = pattern_mat_vec[song_list[ind]]
# oracle_vec = vmo_dict[song_list[ind]]
ref_pttr = pttr_vec[0][:]
per_pttr = pttr_vec[1:5][:]
add_pttr_list = []
pttr = per_pttr.pop()
# min_len = int(np.mean(oracle_vec[0].lrs)/2)
while per_pttr != []:
for i,p in enumerate(pttr):
