  This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly
detection in machine operating sounds (ADMOS). As did for our previous ToyADMOS
dataset, we collected a large number of operating sounds of miniature machines
(toys) under normal and anomaly conditions by deliberately damaging them but
extended with providing controlled depth of damages in anomaly samples. Since
typical application scenarios of ADMOS often require robust performance under
domain-shift conditions, the ToyADMOS2 dataset is designed for evaluating
systems under such conditions. The released dataset consists of two
sub-datasets for machine-condition inspection: fault diagnosis of machines with
geometrically fixed tasks and fault diagnosis of machines with moving tasks.
Domain shifts are represented by introducing several differences in operating
conditions, such as the use of the same machine type but with different machine
models and parts configurations, different operating speeds, microphone
arrangements, etc. Each sub-dataset contains over 27 k samples of normal
machine-operating sounds and over 8 k samples of anomalous sounds recorded with
five to eight microphones. The dataset is freely available for download at
https://github.com/nttcslab/ToyADMOS2-dataset and
https://doi.org/10.5281/zenodo.4580270.
