From 29216dcc073f0e763bd28b21f5a16aca0082586a Mon Sep 17 00:00:00 2001 From: Pramod Date: Mon, 25 Feb 2019 19:27:38 +0100 Subject: [PATCH] Add files --- ABE_CVAE_ICASSP19/2_CVAE_training/Train_CVAE.py | 8 ++++---- ABE_CVAE_ICASSP19/2_CVAE_training/cvae.py | 3 +-- ABE_CVAE_ICASSP19/Readme.md | 3 +++ ABE_SSAE_IS18/Readme.md | 3 +++ SWBE_LPAS_ICASSP18/Readme.md | 3 +++ 5 files changed, 14 insertions(+), 6 deletions(-) diff --git a/ABE_CVAE_ICASSP19/2_CVAE_training/Train_CVAE.py b/ABE_CVAE_ICASSP19/2_CVAE_training/Train_CVAE.py index c218dd7..22690e7 100644 --- 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 index b231e58..c146af5 100644 --- 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 index 4a6f33b..06e8b8e 100644 --- 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 index c48e53b..23d8d8a 100644 --- 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 index f3984c7..b1a83c2 100644 --- 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. ____________________________________________________________________________