ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
This provides a resource for researchers in acoustic signal processing to develop anomaly detection methods, though it is incremental as it focuses on data creation rather than new algorithms.
The paper introduces ToyADMOS, a dataset for anomalous sound detection in machine operating sounds, addressing the lack of large-scale data by collecting over 180 hours of normal sounds and over 4,000 anomalous samples from damaged miniature machines.
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