dcase2021_task2_mobile_net_v2

MobileNetV2-based baseline system for DCASE2021 Challenge Task 2.

Description

This system consists of two main scripts:

• 00_train.py
  • "Development" mode:
    • This script trains a model for each machine type by using the directory dev_data/<machine_type>/train/.
  • "Evaluation" mode:
    • This script trains a model for each machine type by using the directory eval_data/<machine_type>/train/. (This directory will be from the "additional training dataset".)
• 01_test.py
  • "Development" mode:
    • This script makes a csv file for each section including the anomaly scores for each wav file in the directories dev_data/<machine_type>/source_test/ and dev_data/<machine_type>/target_test/.
    • The csv files are stored in the directory result/.
    • It also makes a csv file including AUC, pAUC, precision, recall, and F1-score for each section.
  • "Evaluation" mode:
    • This script makes a csv file for each section including the anomaly scores for each wav file in the directories eval_data/<machine_type>/source_test/ and eval_data/<machine_type>/target_test/. (These directories will be from the "evaluation dataset".)
    • The csv files are stored in the directory result/.

Usage

1. Clone repository

Clone this repository from Github.

2. Download datasets

We will launch the datasets in three stages. So, please download the datasets in each stage:

• "Development dataset"
  • Download dev_data_<machine_type>.zip from https://zenodo.org/record/4562016.
• "Additional training dataset", i.e. the evaluation dataset for training
  • After April. 1, 2021, download eval_data_train_<machine_type>.zip from https://zenodo.org/record/4660992.
• "Evaluation dataset", i.e. the evaluation dataset for test
  • After June. 1, 2021, download eval_data_test_<machine_type>.zip from https://zenodo.org/record/4884786.

3. Unzip dataset

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

• /dcase2021_task2_mobile_net_v2
  • /00_train.py
  • /01_test.py
  • /common.py
  • /keras_model.py
  • /baseline.yaml
  • /readme.md
• /dev_data
  • /fan
    • /train (Normal data in the source and target domains for all sections are included.)
      • /section_00_source_train_normal_0000_.wav
      • ...
      • /section_00_source_train_normal_0999_.wav
      • /section_00_target_train_normal_0000_.wav
      • /section_00_target_train_normal_0001_.wav
      • /section_00_target_train_normal_0002_.wav
      • /section_01_source_train_normal_0000_.wav
      • ...
      • /section_02_target_train_normal_0002_.wav
    • /source_test (Normal and anomaly data in the source domain for all sections are included.)
      • /section_00_source_test_normal_0000.wav
      • ...
      • /section_00_source_test_normal_0099.wav
      • /section_00_source_test_anomaly_0000.wav
      • ...
      • /section_00_source_test_anomaly_0099.wav
      • /section_01_source_test_normal_0000.wav
      • ...
      • /section_02_source_test_anomaly_0099.wav
    • /target_test (Normal and anomaly data in the target domain for all sections are included.)
      • /section_00_target_test_normal_0000.wav
      • ...
      • /section_00_target_test_normal_0099.wav
      • /section_00_target_test_anomaly_0000.wav
      • ...
      • /section_00_target_test_anomaly_0099.wav
      • /section_01_target_test_normal_0000.wav
      • ...
      • /section_02_target_test_anomaly_0099.wav
  • /gearbox (The other machine types have the same directory structure as fan.)
  • /pump
  • /slider
  • /valve
  • /ToyCar
  • /ToyTrain
• /eval_data (Add this directory after launch)
  • /fan
    • /train (Unzipped additional training dataset. Normal data in the source and target domains for all sections are included.)
      • /section_03_source_train_normal_0000_.wav
      • ...
      • /section_03_source_train_normal_0999_.wav
      • /section_03_target_train_normal_0000_.wav
      • /section_03_target_train_normal_0001_.wav
      • /section_03_target_train_normal_0002_.wav
      • /section_04_source_train_normal_0000_.wav
      • ...
      • /section_05_target_train_normal_0002_.wav
    • /source_test (Unzipped evaluation dataset. Normal and anomaly data in the source domain for all sections are included.)
      • /section_03_source_test_0000.wav
      • ...
      • /section_03_source_test_0199.wav
      • /section_04_source_test_0000.wav
      • ...
      • /section_05_source_test_0199.wav
    • /target_test (Unzipped evaluation dataset. Normal and anomaly data in the target domain for all sections are included.)
      • /section_03_target_test_0000.wav
      • ...
      • /section_03_target_test_0199.wav
      • /section_04_target_test_0000.wav
      • ...
      • /section_05_target_test_0199.wav
  • /gearbox (The other machine types have the same directory structure as fan.)
  • /pump
  • /slider
  • /valve
  • /ToyCar
  • /ToyTrain

4. Change parameters

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

5. Run training script (for the development dataset)

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

$ python3.6 00_train.py -d

Options:

Argument		Description
-h	--help	Application help.
-v	--version	Show application version.
-d	--dev	Mode for the development dataset
-e	--eval	Mode for the additional training and evaluation datasets

00_train.py trains a model for each machine type and store the trained models in the directory model/.

6. Run test script (for the development dataset)

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

$ python3.6 01_test.py -d

The options for 01_test.py are the same as those for 00_train.py. 01_test.py calculates an anomaly score for each wav file in the directories dev_data/<machine_type>/source_test/ and dev_data/<machine_type>/target_test/. A csv file for each section including the anomaly scores will be stored in the directory result/. If the mode is "development", the script also outputs another csv file including AUC, pAUC, precision, recall, and F1-score for each section.

7. Check results

You can check the anomaly scores in the csv files anomaly_score_<machine_type>_section_<section_index>_<domain>_test.csv in the directory result/. Each anomaly score corresponds to a wav file in the directories dev_data/<machine_type>/source_test/ and dev_data/<machine_type>/target_test/:

anomaly_score_fan_section_00_source_test.csv

section_00_source_test_normal_0000.wav	-5.492875
section_00_source_test_normal_0001.wav	-13.004328
section_00_source_test_normal_0002.wav	-7.7093716
section_00_source_test_normal_0003.wav	-6.13771
section_00_source_test_normal_0004.wav	-4.9352393
section_00_source_test_normal_0005.wav	-3.93735
  ...

Also, anomaly detection results after thresholding can be checked in the csv files anomaly_score_<machine_type>_section_<section_index>_<domain>_test.csv:

decision_result_fan_section_00_source_test.csv

section_00_source_test_normal_0000.wav	0
section_00_source_test_normal_0001.wav	0
section_00_source_test_normal_0002.wav	0
section_00_source_test_normal_0003.wav	0
section_00_source_test_normal_0004.wav	0
section_00_source_test_normal_0005.wav	1
  ...

Also, you can check performance indicators such as AUC, pAUC, precision, recall, and F1 score:

result.csv

fan						
section	domain	AUC	pAUC	precision	recall	F1 score
0	source	0.4749	0.525263158	0.75	0.03	0.057692308
1	source	0.8041	0.796842105	1	0.18	0.305084746
2	source	0.7661	0.771578947	0.964285714	0.54	0.692307692
0	target	0.5651	0.576315789	1	0.1	0.181818182
1	target	0.75	0.62	0.840909091	0.37	0.513888889
2	target	0.6067	0.6	1	0.08	0.148148148
arithmetic mean		0.66115	0.648333333	0.925865801	0.216666667	0.316489994
harmonic mean		0.63790168	0.633610854	0.914695559	0.090987059	0.165510386
  ...
valve						
section	domain	AUC	pAUC	precision	recall	F1 score
0	source	0.615	0.556842105	1	0.01	0.01980198
1	source	0.5735	0.501052632	1	0.01	0.01980198
2	source	0.5833	0.516842105	0	0	0
0	target	0.5391	0.515789474	0	0	0
1	target	0.7371	0.617894737	0.607407407	0.82	0.69787234
2	target	0.5277	0.507894737	0	0	0
arithmetic mean		0.59595	0.536052632	0.434567901	0.14	0.122912717
harmonic mean		0.588771731	0.533212354	4.44E-16	4.44E-16	4.44E-16
						
		AUC	pAUC	precision	recall	F1 score
arithmetic mean over all machine types, sections, and domains		0.616885065	0.571176451	0.485820851	0.203142415	0.220250915
harmonic mean over all machine types, sections, and domains		0.59239137	0.559521566	6.22E-16	6.22E-16	6.22E-16

8. Run training script for the additional training dataset (after April 1, 2021)

After the additional training dataset is launched, download and unzip it. Move it to eval_data/<machine_type>/train/. Run the training script 00_train.py with the option -e.

$ python3.6 00_train.py -e

Models are trained by using the additional training dataset eval_data/<machine_type>/train/.

9. Run test script for the evaluation dataset (after June 1, 2021)

After the evaluation dataset for test is launched, download and unzip it. Move it to eval_data/<machine_type>/source_test/ and eval_data/<machine_type>/target_test/. Run the test script 01_test.py with the option -e.

$ python3.6 01_test.py -e

Anomaly scores are calculated using the evaluation dataset, i.e., eval_data/<machine_type>/source_test/ and eval_data/<machine_type>/target_test/. The anomaly scores are stored as csv files in the directory result/. You can submit the csv files for the challenge. From the submitted csv files, we will calculate AUC, pAUC, and your ranking.

Dependency

We develop the source code on Ubuntu 16.04 LTS and 18.04 LTS. In addition, we checked performing on Ubuntu 16.04 LTS, 18.04 LTS, CentOS 7, and Windows 10.

Software packages

• p7zip-full
• Python == 3.6.5
• FFmpeg

Python packages

• Keras == 2.3.0
• Keras-Applications == 1.0.8
• Keras-Preprocessing == 1.0.5
• matplotlib == 3.0.3
• numpy == 1.18.1
• PyYAML == 5.1
• scikit-learn == 0.22.2.post1
• scipy == 1.1.0
• librosa == 0.6.0
• audioread == 2.1.5 (more)
• setuptools == 41.0.0
• tensorflow == 1.15.0
• tqdm == 4.43.0

Citation

If you use this baseline system, please cite all the following three papers:

• Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, and Takashi Endo, "Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions," in arXiv e-prints: 2106.04492, 2021. URL
• Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito, "ToyADMOS2: Another Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection under Domain Shift Conditions," in arXiv e-prints: 2106.02369, 2021. URL
• Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi, "MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions," in arXiv e-prints: 2105.02702, 2021. URL