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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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DCASE 2022 Challenge Task 2 Additional Training Dataset

Authors: Dohi, Kota; Imoto, Keisuke; Koizumi, Yuma; Harada, Noboru; Niizumi, Daisuke; Nishida, Tomoya; Purohit, Harsh; +3 Authors

DCASE 2022 Challenge Task 2 Additional Training Dataset

Abstract

Description This dataset is the "additional training dataset" for the DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques". Condition of use This dataset was created jointly by Hitachi, Ltd. and NTT Corporation and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Citation If you use this dataset, please cite all the following three papers. Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, 2022. [URL] Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. In arXiv e-prints: 2205.13879, 2022. [URL] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL] Contact If there is any problem, please contact us: Kota Dohi, kota.dohi.gr@hitachi.com Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com Keisuke Imoto, keisuke.imoto@ieee.org

Related Organizations
Keywords

DCASE, acoustic condition monitoring, acoustic signal processing, machine fault diagnosis, acoustic event detection, unsupervised learning, anomaly detection, sound, machine learning, domain shift, computational auditory scene analysis, audio, acoustic scene classification, anomalous sound detection

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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