Transformer Based Machine Fault Detection From Audio Input
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 (...
| Authors | Kiran Voderhobli Holla |
|---|---|
| Primary category | cs.SD |
| Published | 14 April 2026 |
| Last updated at source | 14 April 2026 |
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