3 papers
cs.SD2025
Self-supervised learning method using multiple sampling strategies for general-purpose audio representation
Ibuki Kuroyanagi, Tatsuya Komatsu
We propose a self-supervised learning method using multiple sampling strategies to obtain general-purpose audio representation. Multiple sampling strategies are used in the propose…
cs.SD2025
Serial-OE: Anomalous sound detection based on serial method with outlier exposure capable of using small amounts of anomalous data for training
Ibuki Kuroyanagi, Tomoki Hayashi, Kazuya Takeda +1
We introduce Serial-OE, a new approach to anomalous sound detection (ASD) that leverages small amounts of anomalous data to improve the performance. Conventional ASD methods rely p…
cs.SD2025
Improving Anomalous Sound Detection through Pseudo-anomalous Set Selection and Pseudo-label Utilization under Unlabeled Conditions
Ibuki Kuroyanagi, Takuya Fujimura, Kazuya Takeda +1
This paper addresses performance degradation in anomalous sound detection (ASD) when neither sufficiently similar machine data nor operational state labels are available. We presen…