# ASDKit: A Toolkit for Comprehensive Evaluation of Anomalous Sound Detection Methods

arXiv: https://arxiv.org/abs/2507.10264 · HTML: https://mlsanity.com/paper/2507.10264/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection](https://mlsanity.com/paper/2603.13749/) (2026)

## Abstract (arXiv)

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.