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MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
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Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies. Hence, there is a rising interest in machine monitoring using different sensors including microphones. In the scientific community, the emergence of public datasets has led to advancements in acoustic detection and classification of scenes and events, but there are no public datasets that focus on the sound of industrial machines under normal and anomalous operating conditions in real factory environments. In this paper, we present a new dataset of industrial machine sounds that we call a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). Normal sounds were recorded for different types of industrial machines (i.e., valves, pumps, fans, and slide rails), and to resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). The purpose of releasing the MIMII dataset is to assist the machine-learning and signal-processing community with their development of automated facility maintenance. The MIMII dataset is freely available for download at: https://zenodo.org/record/3384388
Forward citations
Cited by 7 Pith papers
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IMPACT: Industrial Machine Perception via Acoustic Cognitive Transformer
With DINOS, a new 1,093-hour industrial-sound dataset, the authors show that pretraining a transformer (IMPACT, an EAT adaptation) on machine audio beats general audio models on 24 of 30 self-built monitoring tasks.
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Explainable anomaly detection for sound spectrograms using pooling statistics with quantile differences
Per-pixel quantile thresholding with excess aggregation matches autoencoder baselines on MIMII sound anomaly detection and provides built-in explanation maps.
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Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description
On MIMII industrial machine sounds, deep SVDD with a 2-D subspace reaches average AUCs of 0.84/0.80/0.69 at 6/0/-6 dB SNR, slightly above a dense autoencoder baseline.
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