DCGAN-for-Unsupervised-Learning

Efficiency and performance still need improvement. Might be helpful.

data downloaded web address: https://zenodo.org/record/3678171

reference:

1. http://dcase.community/challenge2020/task-unsupervised-detection-of-anomalous-sounds#baseline-system
2. Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada, and Keisuke Imoto. ToyADMOS: a dataset of miniature-machine operating sounds for anomalous sound detection. In Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 308–312. November 2019. URL: https://ieeexplore.ieee.org/document/8937164.
3. Harsh Purohit, Ryo Tanabe, Takeshi Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi. MIMII Dataset: sound dataset for malfunctioning industrial machine investigation and inspection. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), 209–213. November 2019. URL: http://dcase.community/documents/workshop2019/proceedings/DCASE2019Workshop_Purohit_21.pdf.
4. Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada. Description and discussion on DCASE2020 challenge task2: unsupervised anomalous sound detection for machine condition monitoring. In arXiv e-prints: 2006.05822, 1–4. June 2020. URL: https://arxiv.org/abs/2006.05822.