ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection

TL;DR

ToyADMOS is a comprehensive, large-scale dataset of miniature machine sounds, including normal and anomalous data, to advance research in anomaly detection for machine operating sounds, especially in toy machines.

Contribution

The paper introduces ToyADMOS, the first large-scale dataset for anomaly detection in miniature machine sounds, facilitating research in this area.

Findings

Over 180 hours of normal sounds per sub-dataset

More than 4,000 anomalous sound samples per sub-dataset

Dataset is publicly available for research use

Abstract

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