ASDKit: A Toolkit for Comprehensive Evaluation of Anomalous Sound Detection Methods
Abstract
In this paper, we introduce ASDKit, a toolkit for anomalous sound detection (ASD) task. Our aim is to facilitate ASD research by providing an open-source framework that collects and carefully evaluates various ASD methods. First, ASDKit provides training and evaluation scripts for a wide range of ASD methods, all handled within a unified framework. For instance, it includes the autoencoder-based official DCASE baseline, representative discriminative methods, and self-supervised learning-based methods. Second, it supports comprehensive evaluation on the DCASE 2020--2024 datasets, enabling careful assessment of ASD performance, which is highly sensitive to factors such as datasets and random seeds. In our experiments, we re-evaluate various ASD methods using ASDKit and identify consistently effective techniques across multiple datasets and trials. We also demonstrate that ASDKit reproduces the state-of-the-art-level performance on the considered datasets.
Cite
@article{arxiv.2507.10264,
title = {ASDKit: A Toolkit for Comprehensive Evaluation of Anomalous Sound Detection Methods},
author = {Takuya Fujimura and Kevin Wilkinghoff and Keisuke Imoto and Tomoki Toda},
journal= {arXiv preprint arXiv:2507.10264},
year = {2025}
}