Developing a reliable anomalous sound detection (ASD) system requires robustness to noise, adaptation to domain shifts, and effective performance with limited training data. Current leading methods rely on extensive labeled data for each target machine type to train feature extractors using Outlier-Exposure (OE) techniques, yet their performance on the target domain remains sub-optimal. In this paper, we present \textit{GenRep}, which utilizes generic feature representations from a robust, large-scale pre-trained feature extractor combined with kNN for domain-generalized ASD, without the need for fine-tuning. \textit{GenRep} incorporates MemMixup, a simple approach for augmenting the target memory bank using nearest source samples, paired with a domain normalization technique to address the imbalance between source and target domains. \textit{GenRep} outperforms the best OE-based approach without a need for labeled data with an Official Score of 73.79\% on the DCASE2023T2 Eval set and demonstrates robustness under limited data scenarios. The code is available open-source.

本研究解决了当前异常声音检测系统在噪声干扰、领域迁移和有限训练数据下的鲁棒性问题。提出的GenRep方法利用了强大的预训练特征提取器生成通用特征表示，以kNN技术进行领域泛化，无需微调，并在DCASE2023T2评估集上取得了73.79%的官方得分，表现出在有限数据情况下的强大鲁棒性。

深度通用表示用于领域泛化的异常声音检测