Anomaly_detection_using_U-net_mask_reconstruction

This is a model for Unsupervised Detection of Anomalous Sounds in Driving Environment.

More indepth explanation about the research can be seen in the Google Drive PDFs[1]: https://drive.google.com/file/d/1zBpyDxoOHeXSF7g31pDpC08_Xj-PLT7r/view?usp=sharing.

Description

The system consists of two main scripts:

• 00_train.py
  • This script trains models for each Machine Type by using the directory dev_data/<Machine_Type>/train/ or eval_data/<Microphone_Number>/train/.
• 01_test.py
  • This script makes csv files for each Machine ID including the anomaly scores for each wav file in the directory dev_data/<Microphone_Number>/test/ or eval_data/<Microphone_Number>/test/.
  • The csv files will be stored in the directory result/.
  • If the mode is "development", it also makes the csv files including the AUC and pAUC for each Machine ID.
• train.ipynb
  • This script trains models for one Machine Type of your choice by using the directory dev_data/<Machine_Type>/train/ or eval_data/<Machine_Type>/train/.

Usage

1. Clone repository

Clone this repository from Github.

2. Dataset structure

Unzip the downloaded files and make the directory structure as follows:

• ./Anomaly_detection_using_U-net_mask_reconstruction
  • /dev_data

    • /Microphone 1

      • /train (Only normal data for all Machine IDs are included.)
        • /normal_id_00_00000000.wav
        • ...
        • /normal_id_00_00000999.wav
      • /test (Normal and anomaly data for all Machine IDs are included.)
        • /normal_id_00_00000000.wav
        • ...
        • /normal_id_00_00000349.wav
        • /anomaly_id_00_00000000.wav
        • ...
        • /anomaly_id_00_00000263.wav

    • /Microphone 2 (The other Microphone Types must have the same directory structure as Microphone 1.)

    • /Microphone 3

    • /Microphone 4

    • /Microphone 5

    • .

    • .

    • /Microphone 8

  • /00_train.py
  • /01_test.py
  • /common.py
  • /keras_model.py
  • /baseline.yaml
  • /readme.md

3. Change parameters

You can change the parameters for feature extraction and model definition by editing baseline.yaml.

4. Run training script (for development dataset)

Run the training script 00_train.py. Use the option -d for the development dataset dev_data/<Microphone_Number>/train/.

$ python 00_train.py -d

Options:

Argument		Description
-h	--help	Application help.
-v	--version	Show application version.
-d	--dev	Mode for "development"
-e	--eval	Mode for "evaluation"

00_train.py trains the models for each Machine Type and saves the trained models in the directory model/.

5. Run test script (for development dataset)

Run the test script 01_test.py. Use the option -d for the development dataset dev_data/<Machine_Type>/test/.

$ python 01_test.py -d

The options for 01_test.py are the same as those for 00_train.py. 01_test.py calculates the anomaly scores for each wav file in the directory dev_data/<Machine_Type>/test/. The csv files for each Microphone_Number including the anomaly scores will be stored in the directory result/. If the mode is "development", the script also makes the csv files including the AUCs and pAUCs for each Machine ID.

6. Check results

You can check the anomaly scores in the csv files in the directory result/. Each anomaly score corresponds to a wav file in the directory dev_data/<Machine_Type>/test/:

anomaly_score_Microphone1_id_00.csv

normal_id_01_00000000.wav	6.95342025
normal_id_01_00000001.wav	6.363580014
normal_id_01_00000002.wav	7.048401741
normal_id_01_00000003.wav	6.151557502
normal_id_01_00000004.wav	6.450118248
normal_id_01_00000005.wav	6.368985477
  ...

Also, you can check the AUC and pAUC scores for each Machine ID:

result.csv

## Dependency
We develop the source code on Windows10.

### Software packages
- p7zip-full
- Python == 3.6.5
- FFmpeg

### Python packages
- Keras                         == 2.1.6
- Keras-Applications            == 1.0.8
- Keras-Preprocessing           == 1.0.5
- matplotlib                    == 3.0.3
- numpy                         == 1.16.0
- PyYAML                        == 5.1
- scikit-learn                  == 0.20.2
- librosa                       == 0.6.0
- audioread                     == 2.1.5 (more)
- setuptools                    == 41.0.0
- tensorflow                    == 1.15.0
- tqdm                          == 4.23.4