论文标题
使用频谱时代信息融合使用异常的声音检测
Anomalous Sound Detection using Spectral-Temporal Information Fusion
论文作者
论文摘要
无监督的异常声音检测旨在从正常声音中检测机器的异常异常。但是，最先进的方法并不总是稳定的，甚至对于同一类型的机器而言，执行截然不同，使其对于一般应用不切实际。本文提出了一种基于频谱融合的自我监督方法，以建模正常声音的特征，从而提高了单个机器（甚至相同类型的）检测异常声音的稳定性和性能一致性。 Dcase 2020挑战任务2的实验数据集表明，在最低AUC（最差的AUC（最差）检测性能方面与最先进的方法相比，10.42 \％和21.13 \％改进，即发光\ _aff。此外，所提出的方法改善了数据集中所有类型的机器类型的AUC（个体的平均性能）。源代码可从https://github.com/liuyoude/stgram_mfn获得
Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of machines from normal sounds. However, the state-of-the-art approaches are not always stable and perform dramatically differently even for machines of the same type, making it impractical for general applications. This paper proposes a spectral-temporal fusion based self-supervised method to model the feature of the normal sound, which improves the stability and performance consistency in detection of anomalous sounds from individual machines, even of the same type. Experiments on the DCASE 2020 Challenge Task 2 dataset show that the proposed method achieved 81.39\%, 83.48\%, 98.22\% and 98.83\% in terms of the minimum AUC (worst-case detection performance amongst individuals) in four types of real machines (fan, pump, slider and valve), respectively, giving 31.79\%, 17.78\%, 10.42\% and 21.13\% improvement compared to the state-of-the-art method, i.e., Glow\_Aff. Moreover, the proposed method has improved AUC (average performance of individuals) for all the types of machines in the dataset. The source codes are available at https://github.com/liuyoude/STgram_MFN
