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This paper proposes a new large-scale dataset called "ToyADMOS2" for anomalydetection in machine operating sounds (ADMOS). As did for our previous ToyADMOSdataset, we collected a large number of operating sounds of miniature machines(toys) under normal and anomaly conditions by deliberately damaging them butextended with providing controlled depth of damages in anomaly samples. Sincetypical application scenarios of ADMOS often require robust performance underdomain-shift conditions, the ToyADMOS2 dataset is designed for evaluatingsystems under such conditions. The released dataset consists of twosub-datasets for machine-condition inspection: fault diagnosis of machines withgeometrically fixed tasks and fault diagnosis of machines with moving tasks.Domain shifts are represented by introducing several differences in operatingconditions, such as the use of the same machine type but with different machinemodels and parts configurations, different operating speeds, microphonearrangements, etc. Each sub-dataset contains over 27 k samples of normalmachine-operating sounds and over 8 k samples of anomalous sounds recorded withfive to eight microphones. The dataset is freely available for download athttps://github.com/nttcslab/ToyADMOS2-dataset andhttps://doi.org/10.5281/zenodo.4580270.

ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions