sound-anomaly-detection-with-autoencoders
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aldente0630
aldente0630
MIMII Sound Anomaly Detection with AutoEncoders
Goals
Requirements
Exploratory Data Analysis
Wave Forms
Short Time Fourier Transform
Spectrograms (dB Scale)
Mel-Spectrograms
Multiple Frames of Spectrograms
References
- MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
- 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
- MIMII Dataset Baseline
- DCASE Challenge 2020: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring
- Performing Anomaly Detection on Industrial Equipment Using Audio Signals
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aldente0630
Metadata
MIMII Sound Anomaly Detection with AutoEncoders