  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
