DCASE2021 Challenge - Task 2 - Machine Condition Monitoring

This is our submission to task 2 of the 2021's DCASE challenge on Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions. For more information visit the official DCASE website.

Setup

To reproduce our experiments we recommend a GPU with at least 11GB of VRAM (e.g. NVIDIA GTX 1080Ti) and at least 32 Gb of main memory.

1. install miniconda.
2. download the data
3. store unzipped data into folder ~/shared/DCASE2021/task2 (or set --data_root parameter accordingly)
4. change dir to root of this project
5. run conda env create -f environment.yaml to install the conda environment
6. activate the conda environment with conda activate dcase2021_task2

Hint: Run your experiments with the --debug flag to check whether your environment is set up properly.

Run Auxiliary Classification Experimentns

To train a classifiers based on auxiliary classification (in this example for machine type fan), simply run:

CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=1 python -m experiments.train multi_section --version auxiliary_classification --proxy_outliers other_machines --proxy_outlier_lambda 0.5 --machine_type fan

Options for proxy_outliers are {none, other_machines}.

After training, fine-tuneing can be done with:

CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=1 python -m experiments.train multi_section --version fine_tune --run_id 97fcd5a6e7d24a8c83f77e286384f5aa --da_task ccsa --da_lambda 1.0 --margin 0.5 --learning_rate 1e-5 --rampdown_length 0 --rampdown_start 3 --max_epochs 3 --proxy_outliers other_machines --machine_type fan

where the run_id parameter has to be replaced with the run_id of the pre-trained model from the previous step.

Run Density Estimation & Reconstruction Error Experiments

In a similar vein, density estimation/ reconstruction error models for machine type fan can be trained with the following command:

CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=1 python -m experiments.train density --version made --architecture made --n_gaussians 1 --proxy_outliers other_sections --proxy_outlier_lambda 1.0 --margin 0.5 --consistent_with_librosa --machine_type fan

You can choose the model type by setting the --architecture parameter {AE, MADE, MAF}. Options for proxy_outliers are {none, other_sections, other_sections_and_machines}. The --consistent_with_librosa flag ensures torchaudio returns the same results as librosa.

Dashboard

To view the training progress/ results, change directory to the log directory (cd logs) and start the mlflow dashboard with mlflow ui. By default, the dashboard will be served at http://127.0.0.1:5000.

References

• 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, 1–5, 2021.
• 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: 2006.05822, 1–4, 2021.
• Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. arXiv preprint arXiv:2106.02369, 2021.

Citation

If you use any parts of the implementation please cite our report:

@techreport{Primus2021DCASEChallenge,
    Author      =   {Primus, Paul and Zwifl, Martin and Widmer, Gerhard},
    institution =   {{DCASE2021 Challenge}},
    title       =   {CP-JKU Submission to DCASE'21: Improving Out-of-Distribution Detectors for Machine Condition Monitoring with Proxy Outliers \& \\ Domain Adaptation via Semantic Alignment},
    month       =   {June},
    year        =   2021
}

If you use the ResNet model or the model implementation please cite the following paper:

@inproceedings{Koutini2019Receptive,
    author      =   {Koutini, Khaled and Eghbal-zadeh, Hamid and Dorfer, Matthias and Widmer, Gerhard},
    title       =   {{The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification}},
    booktitle   =   {Proceedings of the European Signal Processing Conference (EUSIPCO)},
    address     =   {A Coru\~{n}a, Spain},
    year        =   2019
}

Links

• DCASE Community
• PyTorch Lightning
• mlflow