ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
Abstract
This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS). To the best our knowledge, no large-scale datasets are available for ADMOS, although large-scale datasets have contributed to recent advancements in acoustic signal processing. This is because anomalous sound data are difficult to collect. To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them. The released dataset consists of three sub-datasets for machine-condition inspection, fault diagnosis of machines with geometrically fixed tasks, and fault diagnosis of machines with moving tasks. Each sub-dataset includes over 180 hours of normal machine-operating sounds and over 4,000 samples of anomalous sounds collected with four microphones at a 48-kHz sampling rate. The dataset is freely available for download at https://github.com/YumaKoizumi/ToyADMOS-dataset
Keywords
Cite
@article{arxiv.1908.03299,
title = {ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection},
author = {Yuma Koizumi and Shoichiro Saito and Hisashi Uematsu and Noboru Harada and Keisuke Imoto},
journal= {arXiv preprint arXiv:1908.03299},
year = {2019}
}
Comments
5 pages, to appear in IEEE WASPAA 2019