pytorch implementation for "Anomalous Sound Detection Using Spectral-Temporal Information Fusion"

The paper is available in link.

Installation

sh run.sh or

$ conda create -n stgram_mfn python=3.7
$ conda activate stgram_mfn
$ pip install -r requirements.txt
$ python run.py

Dataset

DCASE2020 Task2 Dataset:

• development dataset
• additional training dataset
• Evaluation dataset

data path can be set in config.yaml

Model Weights File

Our trained model weights file for loading can be get in https://zenodo.org/record/7194640#.Y0t1WXZBxD8

Result on development dataset

ToyCar      
id  AUC pAUC
1   0.830719697 0.652198679
2   0.951617251 0.874308413
3   0.995218329 0.981046957
4   0.99993531  0.999659526
Average 0.944372647 0.876803394
ToyConveyor     
id  AUC pAUC
1   0.869696875 0.766776316
2   0.641580986 0.547164566
3   0.725856264 0.594070052
Average 0.745711375 0.636003645
fan     
id  AUC pAUC
0   0.950638821 0.894607526
2   0.996852368 0.983433514
4   0.813936782 0.681034483
6   0.999972299 0.999854206
Average 0.940350067 0.889732432
pump        
id  AUC pAUC
0   0.892342657 0.744939271
2   0.83481982  0.693219535
4   0.9999  0.999473684
6   0.950490196 0.832301342
Average 0.919388168 0.817483458
slider      
id  AUC pAUC
0   1   1
2   0.982209738 0.906367041
4   0.99988764  0.999408634
6   0.999775281 0.998817268
Average 0.995468165 0.976148236
valve       
id  AUC pAUC
0   1   1
2   0.988333333 0.952192982
4   1   1
6   0.99725 0.985526316
Average 0.996395833 0.984429825
Total Average   0.923614376 0.863433498

Cite

If you think this work is useful to you, please cite:

@INPROCEEDINGS{9747868,
  author={Liu, Youde and Guan, Jian and Zhu, Qiaoxi and Wang, Wenwu},
  booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Anomalous Sound Detection Using Spectral-Temporal Information Fusion}, 
  year={2022},
  volume={},
  number={},
  pages={816-820},
  doi={10.1109/ICASSP43922.2022.9747868}}