Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning

Takuya Fujimura; Gordon Wichern; Yoshiki Masuyama; Christoph Boeddeker; Kohei Saijo; Julius Richter; Takahiro Edo; Jonathan Le Roux

arXiv · 2608.00447

Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning

Takuya Fujimura·Gordon Wichern·Yoshiki Masuyama·Christoph Boeddeker·Kohei Saijo·Julius Richter·Takahiro Edo·Jonathan Le Roux

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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%.

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Takuya Fujimura, Gordon Wichern, Yoshiki Masuyama, Christoph Boeddeker, Kohei Saijo, Julius Richter, Takahiro Edo, Jonathan Le Roux. 2026-08-01. Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning. https://arxiv.org/abs/2608.00447

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