readme update

README.md

@@ -27,18 +27,19 @@ You can use command **pip install -r requirements.txt** to install all packages
 
 ## How to use
 1. install [**python3.x**](https://www.python.org/) or [**Anaconda**](https://repo.continuum.io/archive/) and add the path into the environment variable (recommand python3.5).
-2. **GPU** run environment [**configure**](https://blog.csdn.net/yhaolpz/article/details/71375762?locationNum=14&fps=1) if train the network (optional).
+2. **GPU** run environment [**configure**](https://blog.csdn.net/yhaolpz/article/details/71375762?locationNum=14&fps=1) for network training (**optional**).
 3. install all dependent packages mentioned above (open **[setup](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/setup)/requirements.txt** and input "**pip install -r requirements**" into your cmd window).
-4. **run** the code as the [**example**](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/src/config_file) as shows
-5. use [**tensorboard**](http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/graph_viz.html) to visualize the train process such as the **accuracy** and **loss curve** of train and validation. The command is "**tensorboard --logdir=/path/to/log-directory**".
+4. **run** the code as the [**example**](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/src/config_file) as shows.
+5. use [**tensorboard**](http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/graph_viz.html) to visualize the training process such as the **accuracy** and **loss curve** of training and validation. The command is "**tensorboard --logdir=/path of log**".
 6. If you want to design your own network based on this project, there is an [**instruction**](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/src/networks/network_design.md) for you.
-7. Our sourcecode is coded with [**Pycharm**](https://github.com/Charleswyt/tf_audio_steganalysis/blob/master/setup/pycharm.md), and the hard wrap is setted as **180**.
+7. Our sourcecode is coded with [**Pycharm**](https://github.com/Charleswyt/tf_audio_steganalysis/blob/master/setup/pycharm.md), and the **hard wrap** is setted as **180**. If you setting of hard wrap is less than 180, there will be warnings shwon in pycharm.
 
 ## File description
 ID      |   File                    |   Function
 :-      |   :-                      |    :-
-1       |   src                     |   source code
-2       |   paper                   |   the PPT and brief introduction of our recent work
-3       |   setup                   |   a **requirements.txt** in this folder, which is used to install all packages in this system
-4       |   jupyter                 |   a folder for jupyter debug
-5       |   data_processing         |   tools which are used for QMDCT coefficients extraction and dataset build
+1       |   audio_samples           |   some audio samples for shown
+2       |   data_processing         |   tools which are used for QMDCT coefficients extraction and dataset build
+3       |   jupyter                 |   a folder for jupyter debug
+4       |   paper                   |   the paper, PPT, dataset and brief introduction of our recent work
+5       |   setup                   |   a **requirements.txt** in this folder, which is used to install all packages in this system
+6       |   src                     |   source code

audio_samples/readme.md

@@ -0,0 +1,10 @@
+## Audio samples
+
+File                            |   Function
+:-                              |    :-
+cover_128.mp3                   |   **cover** mp3 audio file with the bitrate of **128 kbps**
+cover_320.mp3                   |   **cover** mp3 audio file with the bitrate of **320 kbps**
+HCM_B_128_ER_10.mp3             |   **stego** mp3 audio file with the stego algorithm of **HCM**, the bitrate of **128 kbps** and RER of 1.0
+HCM_B_320_ER_10.mp3             |   **stego** mp3 audio file with the stego algorithm of **HCM**, the bitrate of **320 kbps** and RER of **1.0**
+EECS_B_128_W_2_H_7_ER_10.mp3    |   **stego** mp3 audio file with the stego algorithm of **EECS**, the bitrate of **128 kbps**, the width of **2**, the height of **7** and RER of **1.0**
+EECS_B_320_W_2_H_7_ER_10.mp3    |   **stego** mp3 audio file with the stego algorithm of **EECS**, the bitrate of **320 kbps**, the width of **2**, the height of **7** and RER of **1.0**

readme.md

@@ -27,18 +27,19 @@ You can use command **pip install -r requirements.txt** to install all packages
 
 ## How to use
 1. install [**python3.x**](https://www.python.org/) or [**Anaconda**](https://repo.continuum.io/archive/) and add the path into the environment variable (recommand python3.5).
-2. **GPU** run environment [**configure**](https://blog.csdn.net/yhaolpz/article/details/71375762?locationNum=14&fps=1) if train the network (optional).
+2. **GPU** run environment [**configure**](https://blog.csdn.net/yhaolpz/article/details/71375762?locationNum=14&fps=1) for network training (**optional**).
 3. install all dependent packages mentioned above (open **[setup](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/setup)/requirements.txt** and input "**pip install -r requirements**" into your cmd window).
-4. **run** the code as the [**example**](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/src/config_file) as shows
-5. use [**tensorboard**](http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/graph_viz.html) to visualize the train process such as the **accuracy** and **loss curve** of train and validation. The command is "**tensorboard --logdir=/path/to/log-directory**".
+4. **run** the code as the [**example**](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/src/config_file) as shows.
+5. use [**tensorboard**](http://wiki.jikexueyuan.com/project/tensorflow-zh/how_tos/graph_viz.html) to visualize the training process such as the **accuracy** and **loss curve** of training and validation. The command is "**tensorboard --logdir=/path of log**".
 6. If you want to design your own network based on this project, there is an [**instruction**](https://github.com/Charleswyt/tf_audio_steganalysis/tree/master/src/networks/network_design.md) for you.
-7. Our sourcecode is coded with [**Pycharm**](https://github.com/Charleswyt/tf_audio_steganalysis/blob/master/setup/pycharm.md), and the hard wrap is setted as **180**.
+7. Our sourcecode is coded with [**Pycharm**](https://github.com/Charleswyt/tf_audio_steganalysis/blob/master/setup/pycharm.md), and the **hard wrap** is setted as **180**. If you setting of hard wrap is less than 180, there will be warnings shwon in pycharm.
 
 ## File description
 ID      |   File                    |   Function
 :-      |   :-                      |    :-
-1       |   src                     |   source code
-2       |   paper                   |   the PPT and brief introduction of our recent work
-3       |   setup                   |   a **requirements.txt** in this folder, which is used to install all packages in this system
-4       |   jupyter                 |   a folder for jupyter debug
-5       |   data_processing         |   tools which are used for QMDCT coefficients extraction and dataset build
+1       |   audio_samples           |   some audio samples for shown
+2       |   data_processing         |   tools which are used for QMDCT coefficients extraction and dataset build
+3       |   jupyter                 |   a folder for jupyter debug
+4       |   paper                   |   the paper, PPT, dataset and brief introduction of our recent work
+5       |   setup                   |   a **requirements.txt** in this folder, which is used to install all packages in this system
+6       |   src                     |   source code

src/readme.md

@@ -1,18 +1,20 @@
 ## File description
 ID      |   File                    |   Function
 :-      |   :-                      |    :-
-01      |   audio_preprocess.py     |   include some pre-process methods for **audio**
-02      |   text_preprocess.py      |   include some pre-process methods for **text**
-03      |   image_preprocess.py     |   include some pre-process methods for **image**
-04      |   distribution.py         |   distribution calculation
-05      |   config.py               |   all configuration and parameters setting for the system running
-06      |   filters.py              |   some **filters** used for pre-processing such as kv kernel or other **rich model**
-07      |   **main.py**             |   the main program
-08      |   manager.py              |   **GPU** management (free GPU selection **automatically**)
-09      |   dataset.py              |   tfrecord read and write
-10      |   layer.py                |   basic unit in CNN such as **conv layer**, **pooling layer**, **BN layer** and so on
-11      |   utils.py                |   some useful tools such as **minibatch**, **get_data_batch**, 
-12      |   run.py                  |   the **train** and **test** of the network **get_weights**, **get_biases** and so on
-13      |   dataset.py              |   some functions of tfrecord read and write
-14      |   networks                |   all designed networks are contained in this folder, audio and image steganalysis, classification
-15      |   config_file             |   three files, config_train, config_test and config_steganalysis, in this folder are uesd to send the paramters into the network, like the usage in Caffe
+01      |   config_file             |   three files, config_train, config_test and config_steganalysis, in this folder are uesd to send the paramters into the network, like the usage in Caffe
+02      |   dct_kernels             |   dct kernels for high-pass filtering (kernel size: 2,3,4,5,6,7,8)
+03      |   matlab_scripts          |   matlab scripts for jpeg image read and write
+04      |   networks                |   all designed networks are contained in this folder, audio and image steganalysis, classification
+05      |   audio_preprocess.py     |   include some pre-process methods for **audio**
+06      |   config.py               |   all configuration and parameters setting for the system running
+07      |   dataset.py              |   some functions of tfrecord read and write
+08      |   distribution.py         |   distribution calculation
+09      |   file_preprocess.py      |   include some pre-process methods for **file**
+10      |   filters.py              |   some **filters** used for pre-processing such as kv kernel or other **rich model**
+11      |   image_preprocess.py     |   include some pre-process methods for **image**
+12      |   layer.py                |   basic unit in CNN such as **conv layer**, **pooling layer**, **BN layer** and so on
+13      |   **main.py**             |   the main program
+14      |   manager.py              |   **GPU** management (free GPU selection **automatically**)
+15      |   run.py                  |   the **train** and **test** of the network **get_weights**, **get_biases** and so on
+16      |   text_preprocess.py      |   include some pre-process methods for **text**
+17      |   utils.py                |   some useful tools such as **minibatch**, **get_data_batch** and so on