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

Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada, Keisuke Imoto
8/9/2019
eess.AScs.LGcs.SD

Official code: YumaKoizumi/ToyADMOS-dataset (118 stars, last commit 2020-02-20).

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

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Code Implementations(1)

YumaKoizumi/ToyADMOS-datasetOfficial100%

Dataset and its sample codes for anomaly detection in sound.

11818PythonJul 25, 20196 years agoNOASSERTION

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Cite this paper

@article{koizumi2019toyadmos,
  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},
  year   = {2019},
  eprint = {1908.03299},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1908.03299}
}

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