If you use the MIMII Dataset, please cite either of the following papers:

[1] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” arXiv preprint arXiv:1909.09347, 2019. URL: https://arxiv.org/abs/1909.09347

[2] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019.

baseline.py---------autoencoder code
baseline.yaml-------autoencoder parameter setting
requirements.txt----package to use MIMII dataset baseline
result--------------folder to save testing result(AE_data_distribution.yaml: data distribution, AE_result_accuracy.yaml: result)
model---------------folder to save model loss
dataset-------------folder to save dataset

Threshold: training average reconstruction error