通过高频增强提升异常声音检测模型对正常低频特征的识别能力。
Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detection
- 在对比学习中引入高频信息增强，引导模型关注低频正常信号。
- 在DCASE 2020上性能优于其他对比学习方法，显著提升检测准确率。
- 方法具有强泛化性，适用于不同数据集的异常声音检测任务。
异常暴露法是解决无监督异常声音检测的有效方法，其核心在于让模型学习正常数据的分布空间。基于生物感知和数据分析发现，异常音频与噪声通常含有更高频率成分。为此，我们提出一种在对比学习中融入高频信息的数据增强方法，使模型更关注音频中的低频部分，该部分反映设备的正常运行状态。我们在DCASE 2020 Task 2数据集上进行了评估，结果表明该方法优于该数据集上其他对比学习方法。此外，在DCASE 2022 Task 2数据集上也验证了该方法的泛化能力。
原文摘要 · Abstract (English)
The outlier exposure method is an effective approach to address the unsupervised anomaly sound detection problem. The key focus of this method is how to make the model learn the distribution space of normal data. Based on biological perception and data analysis, it is found that anomalous audio and noise often have higher frequencies. Therefore, we propose a data augmentation method for high-frequency information in contrastive learning. This enables the model to pay more attention to the low-frequency information of the audio, which represents the normal operational mode of the machine. We evaluated the proposed method on the DCASE 2020 Task 2. The results showed that our method outperformed other contrastive learning methods used on this dataset. We also evaluated the generalizability of our method on the DCASE 2022 Task 2 dataset.
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