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cs.SD2025
Joint Analysis of Acoustic Scenes and Sound Events Based on Semi-Supervised Training of Sound Events With Partial Labels
Keisuke Imoto
Annotating time boundaries of sound events is labor-intensive, limiting the scalability of strongly supervised learning in audio detection. To reduce annotation costs, weakly-super…
cs.SD2025
Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi +9
This paper introduces the task description for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge Task 2, titled "First-shot unsupervised anomalo…
cs.SD2024
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection
Yoto Fujita, Yoshiaki Bando, Keisuke Imoto +2
This paper describes sound event localization and detection (SELD) for spatial audio recordings captured by firstorder ambisonics (FOA) microphones. In this task, one may train a d…