ASDKit统一评估异常声音检测方法，提升实验可复现性。
ASDKit: A Toolkit for Comprehensive Evaluation of Anomalous Sound Detection Methods
- 统一框架整合多种异常声音检测方法的训练与评估
- 在DCASE 2020–2024数据集上复现了顶尖性能结果
- 揭示跨数据集、多随机种子下稳定有效的技术
本文介绍ASDKit，一个面向异常声音检测（ASD）任务的工具包。其目标是通过提供开源框架，汇聚并严谨评估多种ASD方法，推动该领域研究发展。ASDKit提供涵盖自编码器基线、判别式方法及自监督学习方法在内的广泛ASD方法的训练与评估脚本，均在统一框架内实现。同时支持在DCASE 2020–2024数据集上的全面评估，以应对数据集和随机种子等因素对性能敏感的问题。实验中，我们利用ASDKit重新评估多种ASD方法，识别出在多个数据集和试验中表现稳定的有效技术，并验证其在所考察数据集上达到当前最优水平。
原文摘要 · Abstract (English)
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.
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