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

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Authors Noboru Harada, Shoichiro Saito, Yuma Koizumi, Keisuke Imoto, Hisashi Uematsu
Journal/Conference Name IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Paper Category
Paper 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
Date of publication 2019
Code Programming Language Python
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