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

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arxiv 1908.03299 v1 pith:ALAVMUNF submitted 2019-08-09 eess.AS cs.LGcs.SDstat.ML

classification eess.AScs.LGcs.SDstat.ML
keywords datasetsoundsanomalousadmoslarge-scalemachinesoperatingavailable
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning

    cs.LG 2025-08 unverdicted novelty 2.0 of 10

    The submitted body is an unrelated survey, not the SHeRL-FL method claimed in the metadata.

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