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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/. as Yohei Kawaguchi mentioned before here are some steps below you should follow to get your results

-after unzipping any version put the code inside a folder called dcase2021_task2_mobile_net_v2 -create a folder called results -create another folder called model -if you want to get same results as version 2 or 3 you should add 30% more data pitch shifted or time stretched using notebook called data-augmenting.py -to run the training process open the prompt and set up your dependencies in an enviroment open the folder dcase2021_task2_mobile_net_v2 -then type 00_train.py -d -to test type 00_test.py

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