# Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers

arXiv: https://arxiv.org/abs/2501.04819 · HTML: https://mlsanity.com/paper/2501.04819/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline](https://mlsanity.com/paper/2303.00455/) (2023)
- [Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2406.07250/) (2024)
- [Anomalous Sound Detection with Machine Learning: A Systematic Review](https://mlsanity.com/paper/2102.07820/) (2021)

## Built on by (most cited first, 2 of 2)

- [From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms](https://mlsanity.com/paper/2502.18328/) (2025)
- [Toward Faithful Explanations in Acoustic Anomaly Detection](https://mlsanity.com/paper/2601.12660/) (2026)

## Abstract (arXiv)

In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies already operating in a very competitive market. Moreover, sawmills are challenging environments for machinery and sensors. Given that experienced machine operators may be able to diagnose defects or malfunctions, one possible way of assisting novice operators is through acoustic monitoring. As a step towards the automation of wood-processing equipment and decision support systems for machine operators, in this paper, we explore using a deep convolutional autoencoder for acoustic anomaly detection of wood planers on a new real-life dataset. Specifically, our convolutional autoencoder with skip connections (Skip-CAE) and our Skip-CAE transformer outperform the DCASE autoencoder baseline, one-class SVM, isolation forest and a published convolutional autoencoder architecture, respectively obtaining an area under the ROC curve of 0.846 and 0.875 on a dataset of real-factory planer sounds. Moreover, we show that adding skip connections and attention mechanism under the form of a transformer encoder-decoder helps to further improve the anomaly detection capabilities.