论文标题
使用无监督和半监督的自动编码器和伽马酮音频表示的异常检测
Anomalous Sound Detection using unsupervised and semi-supervised autoencoders and gammatone audio representation
论文作者
论文摘要
如今，异常的声音检测（ASD）是机器侦听学科中的主题主题之一。由于其在许多领域的直接适用性，无监督的检测引起了很多兴趣。例如，与工业过程有关，机器中发生故障或损坏的早期发现可能意味着节省大量，并提高工业过程的效率。可以使用无监督的ASD解决方案来解决此问题，因为工业机器在培训阶段将这些音频数据仅通过将这些音频数据损坏。本文提出了一个基于卷积自动编码器（无监督和半监督）和基于γ的音频表示的新颖框架。这些体系结构获得的结果大大超过了作为基线的结果。
Anomalous sound detection (ASD) is, nowadays, one of the topical subjects in machine listening discipline. Unsupervised detection is attracting a lot of interest due to its immediate applicability in many fields. For example, related to industrial processes, the early detection of malfunctions or damage in machines can mean great savings and an improvement in the efficiency of industrial processes. This problem can be solved with an unsupervised ASD solution since industrial machines will not be damaged simply by having this audio data in the training stage. This paper proposes a novel framework based on convolutional autoencoders (both unsupervised and semi-supervised) and a Gammatone-based representation of the audio. The results obtained by these architectures substantially exceed the results presented as a baseline.
