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

ToyADMOS2 dataset is a large-scale dataset for anomaly detection in machine operating sounds (ADMOS), designed for evaluating systems under domain-shift conditions. It consists of two sub-datasets for machine-condition inspection: fault diagnosis of machines with geometrically fixed tasks ("toy car") and fault diagnosis of machines with moving tasks ("toy train"). 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 part 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 at a 48-kHz sampling rate. A subset of the ToyADMOS2 dataset was used in the DCASE 2021 challenge task 2: Unsupervised anomalous sound detection for machine condition monitoring under domain shifted conditions. What makes this dataset different from others is that it is not used as is, but in conjunction with the tool provided on GitHub. The mixer tool lets you create datasets with any combination of recordings by describing the amount you need in a recipe file. The samples are compressed as MPEG-4 ALS (MPEG-4 Audio Lossless Coding) with a suffix of '.mp4' that you can load by using the audioread or librosa python module. The total size of files under a folder ToyADMOS2 is 149 GB, and the total size of example benchmark datasets that are created from the ToyADMOS2 dataset is 13.2 GB. The detail of the dataset is described in [1] and GitHub: https://github.com/nttcslab/ToyADMOS2-dataset License: see LICENSE.pdf for the detail of the license. [1] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito, "ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions," 2021. https://arxiv.org/abs/2106.0236