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eess.AS2026
Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning
Takuya Fujimura, Gordon Wichern, Yoshiki Masuyama +5
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 re…
eess.AS2026
NABEATs: Noise-Aware Audio Representation Learning
Takuya Fujimura, Yoshiki Masuyama, Gordon Wichern +3
We propose the concept of noise-aware audio self-supervised learning (SSL), whose goal is to encode audio mixtures while suppressing undesired noise, and present Noise-Aware BEATs…
eess.AS2026
Pseudo-label distillation for discriminative anomalous sound detection
Takuya Fujimura, Tomoki Toda
Discriminative anomalous sound detection (ASD) methods train a feature extractor through a classification task using machine-information labels. They then detect anomalies in the r…