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bachhavpramod · Feb 25, 2019 · 29216dc · 29216dc
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diff --git a/ABE_CVAE_ICASSP19/2_CVAE_training/Train_CVAE.py b/ABE_CVAE_ICASSP19/2_CVAE_training/Train_CVAE.py
@@ -19,12 +19,12 @@
 #
 #     https://blog.keras.io/building-autoencoders-in-keras.html
 #        - Here, note that KL loss definition has a minor mistake - 
-#        - KL-loss = -0.5 K.sum .... instead of KL-loss = -0.5 K.sum
-#     Better version => 
+#        - It should be KL-loss = -0.5 K.sum() .... instead of KL-loss = -0.5 K.mean()
+#     Better version can be found at => 
 #     https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py
 #
-#    A very nice understanding about Kingma's papers on VAE at
-#     [http://bjlkeng.github.io/posts/variational-autoencoders/](http://bjlkeng.github.io/posts/variational-autoencoders/)
+#    A very nice understanding about Kingma's papers on VAE at 
+#     http://bjlkeng.github.io/posts/variational-autoencoders/
 
 ##############################################################################################
 

diff --git a/ABE_CVAE_ICASSP19/2_CVAE_training/cvae.py b/ABE_CVAE_ICASSP19/2_CVAE_training/cvae.py
@@ -206,8 +206,7 @@ def init_feedforward(self, L, activations, pDrop, BN, last_layer_name):
 #                    model.add(BatchNormalization())
 #                model.add(Activation(self.activations[i-1]))              
 #                if self.BN=='a':
-#                    model.add(BatchNormalization())
-#                    
+#                    model.add(BatchNormalization())                   
 #                if i is len(L)-1:     
 #                    model.add(Activation(self.activations[i-1],  name = last_layer_name))              
 #                if self.BN=='a':

diff --git a/ABE_CVAE_ICASSP19/Readme.md b/ABE_CVAE_ICASSP19/Readme.md
@@ -1,3 +1,6 @@
+# Latent Representation Learning for Artificial Bandwidth Extension (ABE) using a Conditional Variational Auto-encoder (CVAE)
+____________________________________________________________________________
+
 # Description:
 These folders contain scripts to perform dimensionality reduction (DR)/ feature extraction using conditional variational auto-encoder (CVAE) and perform artificial bandwidth extension using [GMM regression](https://github.com/bachhavpramod/bandwidth_extension/blob/master/utilities/GMMR.m). 
 ____________________________________________________________________________

diff --git a/ABE_SSAE_IS18/Readme.md b/ABE_SSAE_IS18/Readme.md
@@ -1,3 +1,6 @@
+# Artificial Bandwidth Extension (ABE) using a Stacked Auto-encoders (SSAE)
+____________________________________________________________________________
+
 # Description:
 These folders contain scripts to perform Artificial Bandwidth Extension with memory inclusion using Semi-supervised Stacked Auto-encoders.
 ____________________________________________________________________________

diff --git a/SWBE_LPAS_ICASSP18/Readme.md b/SWBE_LPAS_ICASSP18/Readme.md
@@ -1,3 +1,6 @@
+# Efficient super-wide bandwidth extension (SWBE) using linear prediction based analysis synthesis (LPAS)
+____________________________________________________________________________
+
 # Description:
 - This folder contains MATLAB scripts to perform super-wide bandwidth extension (SWBE) using linear prediction based analysis-synthesis.
 ____________________________________________________________________________