Data Augmentation by Finite Element Analysis for Enhanced Machine Anomalous Sound Detection
Current data augmentation methods for machine anomalous sound detection (MASD) suffer from insufficient data generated by real world machines. Open datasets such as audioset are not tailored for machine sounds, and fake sounds created by generative models are not trustworthy. In this paper, we explore a novel data augmentation method in MASD using machine sounds simulated by finite element analysis (FEA). We use Ansys, a software capable for acoustic simulation based on FEA, to generate machine sounds for further training. The physical properties of the machine, such as geometry and material, and the material of the medium is modified to acquire data from multiple domains. The experimental results on DCASE 2023 Task 2 dataset indicates a better performance from models trained using augmented data.
