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 …

The operation state of machine can be monitored by performing anomalous sound detection
(ASD). Unsupervised-ASD is a detection task in which the model detects unknown …

When training a model for anomalous sound detection, one usually needs to estimate the
underlying distribution of the normal data. By doing so, anomalous data has a lower …

Mechanical noise identification and classification are essential for automotive and
machinery fault diagnosis. The scarcity of labelled audio data for noise-related mechanical …

In this paper, a self-supervised representation learning method is proposed for anomalous
sound detection (ASD). ASD has received much research attention in recent DCASE …

This paper proposes an approach for anomalous sound detection that incorporates outlier
exposure and inlier modeling within a unified framework by multitask learning. While outlier …

In this paper, we propose an effective anomalous sound detection (ASD) method based on
representation learning with simulated anomalies. Recently, ASD systems have used Outlier …

This paper proposes an unsupervised anomalous sound detection method using sound
separation. In factory environments, background noise and non-objective sounds obscure …

Abstract Anomalous Sound Detection (ASD) system identifies sound waves from sensors to
detect the anomaly of industrial machines. However, recent methods have failed to …

Unsupervised anomalous sound detection (ASD) aims to identify unknown abnormal
sounds in machines using samples of normal sounds. Most unsupervised ASD methods …