105 citations · 292 across the 35 of their papers we have counts for
5 papers · 1 filter
Streaming Active Learning for Regression Problems Using Regression via Classification
Shota Horiguchi, Kota Dohi, Yohei Kawaguchi
One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes. To maintain the performance, streaming ac…
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…
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection
Tomoya Nishida, Takashi Endo, Yohei Kawaguchi
Semi-supervised anomaly detection~(SSAD) is a task where normal data and a limited number of anomalous data are available for training. In practical situations, SSAD methods suffer…