用Transformer分析机器音频，比传统CNN更准
Transformer Based Machine Fault Detection From Audio Input
- 用Transformer直接处理音频频谱图，无局部性限制
- 在故障检测任务上性能优于传统CNN模型
- 适合对声学故障诊断有需求的工业场景
近年来，声音AI被越来越多地用于预测机器故障。通过在目标机器上安装麦克风，可实时获取其运行状态的声音数据。传统上，卷积神经网络（CNN）被用于分析由声音生成的频谱图，并判断机器是否正常工作。尽管CNN在实践中表现良好，但其固有的局部感受野和参数共享机制可能并不完全适用于频谱图分析。自2020年Vision Transformer（ViT）在图像处理领域取得成功以来，基于Transformer的模型在声音AI领域也受到广泛关注。由于这类模型具有更低的归纳偏置，在数据充足时有望超越CNN在频谱图分析中的表现。本文验证了Transformer架构在声音数据分析中的有效性，并对比了其生成的特征嵌入与CNN在机器故障检测任务上的表现。
原文摘要 · Abstract (English)
In recent years, Sound AI is being increasingly used to predict machine failures. By attaching a microphone to the machine of interest, one can get real time data on machine behavior from the field. Traditionally, Convolutional Neural Net (CNN) architectures have been used to analyze spectrogram images generated from the sounds captured and predict if the machine is functioning as expected. CNN architectures seem to work well empirically even though they have biases like locality and parameter-sharing which may not be completely relevant for spectrogram analysis. With the successful application of transformer-based models in the field of image processing starting with Vision Transformer (ViT) in 2020, there has been significant interest in leveraging these in the field of Sound AI. Since transformer-based architectures have significantly lower inductive biases, they are expected to perform better than CNNs at spectrogram analysis given enough data. This paper demonstrates the effectiveness of transformer-driven architectures in analyzing Sound data and compares the embeddings they generate with CNNs on the specific task of machine fault detection.
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