17th AIAI 2021, 25 - 27 June 2021, Greece

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Gabriel Coelho, Pedro Pereira, Luis Matos, Alexandrine Ribeiro, Eduardo Nunes, André Ferreira, Paulo Cortez, Andre Pilastri


  Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.  

*** Title, author list and abstract as seen in the Camera-Ready version of the paper that was provided to Conference Committee. Small changes that may have occurred during processing by Springer may not appear in this window.