savepath = saveFolder + filename
tmp = loadmat(stftFilePathList[i])
Y1 = np.ndarray.transpose(tmp['HD'])
filename2 = stftFilePathList2[i][-11:-4]
tmp = loadmat(stftFilePathList2[i])
Y2 = np.ndarray.transpose(tmp['HD'])
filename3 = stftFilePathList3[i][-11:-4]
tmp = loadmat(stftFilePathList3[i])
Y3 = np.ndarray.transpose(tmp['HD'])
'''
==== scale the data
'''
[y1_0, dump, dump] = scaleData(Y1[:, 0])
[y1_1, dump, dump] = scaleData(Y1[:, 1])
[y1_2, dump, dump] = scaleData(Y1[:, 2])
[y2_0, dump, dump] = scaleData(Y2[:, 0])
[y2_1, dump, dump] = scaleData(Y2[:, 1])
[y2_2, dump, dump] = scaleData(Y2[:, 2])
[y3_0, dump, dump] = scaleData(Y3[:, 0])
[y3_1, dump, dump] = scaleData(Y3[:, 1])
[y3_2, dump, dump] = scaleData(Y3[:, 2])
hh = np.add(np.add(y1_0, y2_0), y3_0)
bd = np.add(np.add(y1_1, y2_1), y3_1)
sd = np.add(np.add(y1_2, y2_2), y3_2)
all = [np.ndarray.flatten(hh), np.ndarray.flatten(bd), np.ndarray.flatten(sd)]
for i in range(0, 200): #len(stftFilePathList)):
tmp = loadmat(cqtFilePathList[i])
X_song = np.ndarray.transpose(tmp['Xcqt'])
tmp = loadmat(pseudoLabelFilePathList[i])
Y_song = np.ndarray.transpose(tmp['HD'])
assert (len(X_song) == len(Y_song)
), 'dimensionality mismatch between CQT and Pseudo-Labels!'
'''
==== Concatenating matrices
'''
X = np.concatenate((X, X_song), axis=0)
Y = np.concatenate((Y, Y_song), axis=0)
'''
==== Training
'''
[y_hh_scaled, dump, dump] = scaleData(Y[:, 0])
[y_kd_scaled, dump, dump] = scaleData(Y[:, 1])
[y_sd_scaled, dump, dump] = scaleData(Y[:, 2])
y_all = np.concatenate((y_hh_scaled, y_kd_scaled, y_sd_scaled), axis=1)
print '==== training ====\n'
#==== feeding CQT and delta CQT
X_diff = np.diff(X, axis=0)
finalRow = np.zeros((1, np.size(X, 1)))
X_diff = np.concatenate((X_diff, finalRow), axis=0)
X_all = np.concatenate((X, X_diff), axis=1)
model.fit(X_all,
y_all,
epochs=50,
batch_size=640,
# thresNvt_hh = thresNvt(Y[:, 0], thresCurve_hh)
# thresNvt_bd = thresNvt(Y[:, 1], thresCurve_bd)
# thresNvt_sd = thresNvt(Y[:, 2], thresCurve_sd)
# dump, onsetInSec_hh = findPeaks(Y[:, 0], thresCurve_hh, fs, hopSize)
# dump, onsetInSec_bd = findPeaks(Y[:, 1], thresCurve_bd, fs, hopSize)
# dump, onsetInSec_sd = findPeaks(Y[:, 2], thresCurve_sd, fs, hopSize)
#
# onsetInBinary_hh = onset2BinaryVector(onsetInSec_hh, len(Y[:, 0]), hopSize, fs)
# onsetInBinary_bd = onset2BinaryVector(onsetInSec_bd, len(Y[:, 1]), hopSize, fs)
# onsetInBinary_sd = onset2BinaryVector(onsetInSec_sd, len(Y[:, 2]), hopSize, fs)
'''
==== Training
'''
[y_hh_scaled, dump, dump] = scaleData(thresCurve_hh)
[y_kd_scaled, dump, dump] = scaleData(thresCurve_bd)
[y_sd_scaled, dump, dump] = scaleData(thresCurve_sd)
y_all = np.concatenate((y_hh_scaled, y_kd_scaled, y_sd_scaled),
axis=1)
#y_all = np.concatenate((onsetInBinary_hh, onsetInBinary_bd, onsetInBinary_sd), axis=1)
print '==== training ====\n'
model.fit(X,
y_all,
epochs=30,
batch_size=640,
callbacks=[tbCallBack])
'''
==== Save the trained DNN model