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

TL;DR

ToyADMOS2 is a comprehensive, large-scale dataset of miniature machine sounds designed to evaluate anomaly detection systems under various domain shift conditions, aiding robust machine fault diagnosis research.

Contribution

The paper introduces ToyADMOS2, a new dataset with controlled damages and diverse operating conditions for improved anomaly detection in machine sounds.

Findings

Dataset includes over 27,000 normal and 8,000 anomalous samples.

Designed for evaluating robustness under domain shifts.

Provides data for both fixed and moving task fault diagnosis.

Abstract

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.