105 citations · 292 across the 35 of their papers we have counts for
10 papers · 1 filter
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…
Stream-based Active Learning for Anomalous Sound Detection in Machine Condition Monitoring
Tuan Vu Ho, Kota Dohi, Yohei Kawaguchi
This paper introduces an active learning (AL) framework for anomalous sound detection (ASD) in machine condition monitoring system. Typically, ASD models are trained solely on norm…
CAPTDURE: Captioned Sound Dataset of Single Sources
Yuki Okamoto, Kanta Shimonishi, Keisuke Imoto +3
In conventional studies on environmental sound separation and synthesis using captions, datasets consisting of multiple-source sounds with their captions were used for model traini…
Anomalous Sound Detection Based on Sound Separation
Kanta Shimonishi, Kota Dohi, Yohei Kawaguchi
This paper proposes an unsupervised anomalous sound detection method using sound separation. In factory environments, background noise and non-objective sounds obscure desired mach…
Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
Kota Dohi, Keisuke Imoto, Noboru Harada +7
We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: ``First-shot unsupervised anomalous sound detection…
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
Kota Dohi, Keisuke Imoto, Noboru Harada +7
We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring ap…
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task
Kota Dohi, Tomoya Nishida, Harsh Purohit +5
We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD). Domain shifts are differences in data distributions that can d…
Environmental Sound Extraction Using Onomatopoeic Words
Yuki Okamoto, Shota Horiguchi, Masaaki Yamamoto +2
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We…
MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions
Ryo Tanabe, Harsh Purohit, Kota Dohi +4
In this paper, we introduce MIMII DUE, a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environ…
MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
Harsh Purohit, Ryo Tanabe, Kenji Ichige +4
Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies. Hence, there is a rising interest in machine monitoring using different sensors…