@article {CHOI:2024:0736-2935:6778,
title = "Integrating Mixup and ArcFace for enhanced anomalous sound detection",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2024",
volume = "270",
number = "5",
publication date ="2024-10-04T00:00:00",
pages = "6778-6785",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2024/00000270/00000005/art00089",
doi = "doi:10.3397/IN_2024_3869",
author = "CHOI, Jihoon and CHOI, Jung-Woo",
abstract = "In machine condition monitoring, accurate detection of anomalous sounds is essential for timely maintenance interventions. Traditional deep neural network models employ classification-based self-supervised learning to discriminate normal and abnormal operational sounds using classification
confidence. However, these models often suffer from overfitting or excessive confidence, limiting their effectiveness. To overcome this, we introduce a hybrid technique that combines the strengths of Mixup and ArcFace techniques. Mixup enhances model generalization by training on mixed sound
data, while ArcFace promotes greater inter-class distances with an angular margin loss. However, there is a challenge in combining these methods because the angular margin for ArcFace is defined for a single target class, and hence, can be hardly defined for the mixed data utilized in Mixup.
The proposed technique employs the class-dependent angular margins based on the mixing ratio of data. SoftMax losses for individual classes are constructed using the class-dependent margins and then integrated into the Mixup loss. When applied to the DCASE2020 Task2 dataset, the proposed method
significantly outperforms the state-of-the-art NoisyArcMix model in anomaly detection. This approach demonstrates the potential of leveraging class-dependent angular margins in mixed data for improved performance in machine condition monitoring.",
}