MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions

05/06/2021
by   Ryo Tanabe, et al.
0

In this paper, we introduce a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions (MIMII DUE). Conventional methods for anomalous sound detection face challenges in practice because the distribution of features changes between the training and operational phases (called domain shift) due to some real-world factors. To check the robustness against domain shifts, we need a dataset with domain shifts, but such a dataset does not exist so far. The new dataset consists of normal and abnormal operating sounds of industrial machines of five different types under two different operational/environmental conditions (source domain and target domain) independent of normal/abnormal, with domain shifts occurring between the two domains. Experimental results show significant performance differences between the source and target domains, and the dataset contains the domain shifts. These results indicate that the dataset will be helpful to check the robustness against domain shifts. The dataset is a subset of the dataset for DCASE 2021 Challenge Task 2 and freely available for download at https://zenodo.org/record/4740355

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

09/20/2019

MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

Factory machinery is prone to failure or breakdown, resulting in signifi...
11/12/2021

Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts

To develop a sound-monitoring system for machines, a method for detectin...
08/09/2019

ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

This paper introduces a new dataset called "ToyADMOS" designed for anoma...
04/05/2022

Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection

Anomaly detection has many important applications, such as monitoring in...
12/20/2017

Incremental Adversarial Domain Adaptation for Continually Changing Environments

Continuous appearance shifts such as changes in weather and lighting con...
11/21/2021

Health Monitoring of Industrial machines using Scene-Aware Threshold Selection

This paper presents an autoencoder based unsupervised approach to identi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.