Author

Takuya Fujimura

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Preprint Aug 2026

Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning

Noise-aware self-supervised learning (NA-SSL) models for noise-aware anomalous sound detection (NA-ASD) are introduced and the effectiveness of the proposed approach is demonstrated across three base SSL models (BEATs, EAT, and Dasheng), both with and without discriminative fine-tuning.

Takuya Fujimura, G. Wichern, Yoshiki Masuyama et al. · 0 citations

Preprint Jul 2026

Pseudo-label distillation for discriminative anomalous sound detection

Pseudo-label distillation not only transfers the performance of SSL models to a compact model but also further improves performance by leveraging available coarse labels and data augmentation.

Takuya Fujimura, Tomoki Toda · 1 citation