Existing contrastive learning methods for anomalous sound detection refine
the audio representation of each audio sample by using the contrast between the
samples' augmentations (e.g., with time or frequency masking). However, they
might be biased by the augmented data, due to the lack of physical properties
of machine sound, thereby limiting the detection performance. This paper uses
contrastive learning to refine audio representations for each machine ID,
rather than for each audio sample. The proposed two-stage method uses
contrastive learning to pretrain the audio representation model by
incorporating machine ID and a self-supervised ID classifier to fine-tune the
learnt model, while enhancing the relation between audio features from the same
ID. Experiments show that our method outperforms the state-of-the-art methods
using contrastive learning or self-supervised classification in overall anomaly
detection performance and stability on DCASE 2020 Challenge Task2 dataset.

本文使用对比学习方法，针对每个机器 ID 而不是每个音频样本优化音频表示，使用自我监督 ID 分类器微调学习模型，增强了同一 ID 音频特征之间的关系，在 DCASE 2020 Challenge Task2 数据集上的实验表明，该方法在整体异常检测性能和稳定性方面胜过了使用对比学习或自我监督分类的最先进方法。