Techniques Improving the Robustness of Deep Learning Models for Industrial Sound Analysis

The field of Industrial Sound Analysis (ISA) aims to automatically identify faults in production machinery or manufactured goods by analyzing audio signals. Publications in this field have shown that the surface condition of metal balls and different types of bulk materials (screws, nuts, etc.) sliding down a tube can be classified with a high accuracy using audio signals and deep neural networks. However, these systems suffer from domain shift, or dataset bias, due to minor changes in the recording setup which may easily happen in real-world production lines. This paper aims at finding methods to increase robustness of existing detection systems to domain shift, ideally without the need to record new data or retrain the models. Through five experiments, we implement a convolutional neural network (CNN) for two publicly available ISA datasets and evaluate transfer learning, data normalization and data augmentation as approaches to deal with domain shift. Our results show that while supervised methods with additional labeled data are the best approach, an unsupervised method that implements data augmentation with adaptive normalization is able to improve the performance by a large margin without the need of retraining neural networks.