"minor": [1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0]
}
MODE['major'] = utils.rotate(MODE['major'], key_ind)
MODE['minor'] = utils.rotate(MODE['minor'], key_ind)
r_co_major = pearsonr(chroma_vector, MODE["major"])
r_co_minor = pearsonr(chroma_vector, MODE["minor"])
# print(r_co_major[0])
# print(r_co_minor[0])
mode = ''
Cmajor_annoatation = (key_ind + 3) % 12
if (r_co_major[0] > r_co_minor[0]):
mode = Cmajor_annoatation
else:
mode = Cmajor_annoatation + 12
mode = utils.lerch_to_str(mode)
# print('mode', mode)
if DB == 'GTZAN':
pred[gen].append(mode)
else:
pred.append(
'?') # you may ignore this when starting with GTZAN dataset
# print(pred[gen])
print("***** Q2 *****")
if DB == 'GTZAN':
label_list, pred_list = list(), list()
print("Genre \taccuracy")
for g in GENRE:
# TODO: Calculate the accuracy for each genre
tonic_note = tonic
max_r = correlation_major
else:
if correlation_minor > max_r:
mode = "minor"
tonic_note = tonic
max_r = correlation_minor
note = Tonic_keys[tonic_note] + " " + mode
pred = utils.str_to_lerch(note)
predictions.append(pred)
except Exception as e:
print(e)
predictions.append(0)
#break
score = 0
for i in range(len(labels)):
score = score + weighted_score(utils.lerch_to_str(labels[i]),
utils.lerch_to_str(predictions[i]))
weighted_accuracy = score / len(labels)
accuracy = accuracy_score(labels,
predictions,
normalize=True,
sample_weight=None)
fout = open('output/Task3_2.txt', 'a')
fout.write("***** Task 3: local key detection *****\n")
fout.write("A-MAPS Overall Accuracy:\t{:.2f}%\n".format(accuracy * 100))
fout.write("A-MAPS Overall Weighted Accuracy:\t{:.2f}%\n".format(
weighted_accuracy * 100))
fout.close()
"cMinor": [1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0]}
mode['cMajor'] = utils.rotate(mode['cMajor'], key_ind)
mode['cMinor'] = utils.rotate(mode['cMinor'], key_ind)
cMajorCoefficient = pearsonr(chroma_vector, mode['cMajor'])
cMinorCoefficient = pearsonr(chroma_vector, mode['cMinor'])
mode = ''
a = (key_ind+3)%12
if (cMajorCoefficient[0] > cMinorCoefficient[0]):
modePred = a
else:
modePred = a+12
modePred = utils.lerch_to_str(modePred)
if DB == 'GTZAN':
pred[gen].append(modePred)
else:
pred.append('?') # you may ignore this when starting with GTZAN dataset
# print(pred[gen])
print("***** Q2 *****")
if DB == 'GTZAN':
label_list, pred_list = list(), list()
print("Genre \taccuracy")
for g in GENRE:
# TODO: Calculate the accuracy for each genre
# Hint: Use label[g] and pred[g]
correct = 0
chroma_vector.pop(0)
chroma_vector.append(temp)
maxRMajor = np.where(coffiListsMaj == np.amax(coffiListsMaj))
maxRMinor = np.where(coffiListsMin == np.amax(coffiListsMin))
maxRMajor = maxRMajor[0][0]
maxRMinor = maxRMinor[0][0]
if coffiListsMaj[maxRMajor] > coffiListsMin[maxRMinor]:
key_ind = (maxRMajor + 3) % 12
else:
key_ind = (maxRMinor + 15) % 24
# print('key_ind: {}\n'.format(key_ind))
modePred = utils.lerch_to_str(key_ind)
if DB == 'GTZAN':
pred[gen].append(modePred)
else:
pred.append(
'?') # you may ignore this when starting with GTZAN dataset
coffiListsMaj = []
coffiListsMin = []
print("***** Q4 *****")
if DB == 'GTZAN':
label_list, pred_list = list(), list()
print("Genre \taccuracy")
for g in GENRE:
