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ampnn
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modified to remove mean energy, might change back

master
David 2020-11-27 18:43:32 +10:30
parent 160c301a8d
commit b821dde612
1 changed files with 8 additions and 5 deletions
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13
vq_vae_conv1d_2stage.py
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@ -157,10 +157,13 @@ features = features[:nb_samples*eband_K].reshape((nb_samples, eband_K))
features *= args.gain
# normalise
train_mean = np.mean(features, axis=0)
features -= train_mean
train_mean = np.mean(features, axis=1)
print(features.shape,train_mean.shape)
for i in range(nb_samples):
features[i,:] -= train_mean[i]
features *= train_scale
print(np.mean(features, axis=0), np.std(features, axis=0))
#print(np.mean(features, axis=0), np.std(features, axis=0))
# reshape into (batch, timesteps, channels) for conv1D. We
# concatentate the training material with same sequence of frames at a
@ -345,8 +348,8 @@ while key != 'q':
for r in range(nb_plots):
plt.subplot(nb_plotsy,nb_plotsx,r+1)
f = frames[r];
plt.plot(10*(train_mean+train[f,:]/train_scale),'g')
plt.plot(10*(train_mean+train_est[f,:]/train_scale),'r')
plt.plot(10*(train_mean[f]+train[f,:]/train_scale),'g')
plt.plot(10*(train_mean[f]+train_est[f,:]/train_scale),'r')
plt.ylim(0,80)
a_mse = np.mean((10*train[f,:]/train_scale-10*train_est[f,:]/train_scale)**2)
t = "f: %d %3.1f" % (f, a_mse)