Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reducing the ability of the model in detecting anomalies from sound. The self-supervised methods are not always stable and perform differently, even for machines of the same type. In addition, the anomalous sound may be short-lived, making it even harder to distinguish from normal sound. This paper proposes an ID constrained Transformer-based autoencoder (IDC-TransAE) architecture with weighted anomaly score computation for unsupervised ASD. Machine ID is employed to constrain the latent space of the Transformer-based autoencoder (TransAE) by introducing a simple ID classifier to learn the difference in the distribution for the same machine type and enhance the ability of the model in distinguishing anomalous sound. Moreover, weighted anomaly score computation is introduced to highlight the anomaly scores of anomalous events that only appear for a short time. Experiments performed on DCASE 2020 Challenge Task2 development dataset demonstrate the effectiveness and superiority of our proposed method.

翻译：无监督异常声音检测（ASD）旨在仅利用正常声音数据时，检测设备中未知的异常声音。自编码器（AE）和基于自监督学习的方法是两种主流方法。然而，基于AE的方法可能受到限制，因为从正常声音中学习的特征也可能拟合异常声音，从而降低了模型从声音中检测异常的能力。自监督方法并不总是稳定，且性能表现各异，即使对于相同类型的机器也是如此。此外，异常声音可能持续时间短暂，使其更难与正常声音区分。本文提出了一种基于ID约束的Transformer自编码器（IDC-TransAE）架构，结合加权异常分数计算，用于无监督ASD。通过引入一个简单的ID分类器，利用机器ID约束基于Transformer的自编码器（TransAE）的潜在空间，以学习同一机器类型下分布的差异，并增强模型区分异常声音的能力。此外，引入了加权异常分数计算，以突出仅短暂出现的异常事件的异常分数。在DCASE 2020挑战赛任务2开发数据集上进行的实验证明了我们提出方法的有效性和优越性。