10 citations · 11 across the 8 of their papers we have counts for
6 papers · 1 filter
Overview and Meta-Analysis of DCASE 2026 Challenge Task 6: Audio Moment Retrieval from Long Audio
Hokuto Munakata, Tatsuya Komatsu, Keisuke Imoto +3
This paper presents an overview of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2026 Challenge Task 6, Audio Moment Retrieval (AMR) from Long Audio. Given…
Description and Discussion on DCASE 2026 Challenge Task 2: Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daiki Takeuchi +6
This paper presents an overview of DCASE 2026 Challenge Task 2, titled "Noise-aware unsupervised anomalous sound detection (UASD) for machine condition monitoring." The task aims t…
Audio-Image Cross-Modal Retrieval with Onomatopoeic Images
Keisuke Imoto, Yamato Kojima, Takao Tsuchiya
Finding sound effects or environmental sounds that match a creator's intended impression remains a largely manual process in multimedia production. This is especially relevant for…
Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Tomoya Nishida, Noboru Harada, Daisuke Niizumi +9
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot unsupervised anomalous sound detection (…
M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation
Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi +4
Contrastive language-audio pre-training (CLAP) enables zero-shot (ZS) inference of audio and exhibits promising performance in several classification tasks. However, conventional a…
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval
Shunsuke Tsubaki, Daisuke Niizumi, Daiki Takeuchi +3
The aim of this research is to refine knowledge transfer on audio-image temporal agreement for audio-text cross retrieval. To address the limited availability of paired non-speech…