jeffchristoffels / cloud-edge-deployment-trade-off-in-anomalous-sound-detection-application

Python code written for Jeff Christoffels' Master Thesis "Cloud-Edge Deployment Trade-off in Anomalous Sound Detection (ASD) Application" submitted to obtain the Master of Engineering: Computer Science degree.

Cloud-Edge Deployment Trade-off in Anomalous Sound Detection (ASD) Application

Python code written for Jeff Christoffels' Master Thesis "Cloud-Edge Deployment Trade-off in Anomalous Sound Detection (ASD) Application" submitted to obtain the Master of Engineering: Computer Science degree.

Baseline models from:

• 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

The code to train and evaluate the autoencoder-based baseline can be found here. The code to train and evaluate the MobileNetV2-based baseline can be found here.