TR2026-165

Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning

    •  Fujimura, T., Wichern, G., Masuyama, Y., Boeddeker, C., Saijo, K., Richter, J., Edo, T., Le Roux, J., "Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning", Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), October 2026.
      BibTeX TR2026-165 PDF
      • @inproceedings{Fujimura2026oct,
      • author = {Fujimura, Takuya and Wichern, Gordon and Masuyama, Yoshiki and Boeddeker, Christoph and Saijo, Kohei and Richter, Julius and Edo, Takahiro and {Le Roux}, Jonathan},
      • title = {{Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning}},
      • booktitle = {Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE)},
      • year = 2026,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2026-165}
      • }
  • MERL Contacts:
  • Research Areas:

    Artificial Intelligence, Machine Learning, Speech & Audio

Abstract:

In this paper, we introduce noise-aware self-supervised learning (NA-SSL) models for noise-aware anomalous sound detection
(NA-ASD). NA-ASD is an ASD task with two-channel audio recordings, where one microphone is located close to the target machine and the other is located farther away to capture noise. For this task, we simulate two-channel recordings using diverse audio datasets and train NA-SSL models to extract clean SSL representations of the close-microphone signal by using the far-microphone recording dominated by background noise as auxiliary information. The NA-SSL models are then used as frontends in the standard ASD framework. Our experimental evaluation on the
DCASE 2026 Challenge Task 2 development dataset demonstrates the effectiveness of the NA-SSL framework across three base SSL models
(BEATs, EAT, and Dasheng), both with and without discriminative fine- tuning. Furthermore, the challenge results proved the effectiveness of the proposed approach, where the NA-BEATs system won the challenge by a large margin, achieving an official score of 70.24%, while the second-place system achieved 65.46%.