1 citations · 1 across the 7 of their papers we have counts for
7 papers
MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System
Harsh Purohit, Tomoya Nishida, Kota Dohi +2
Insufficient recordings and the scarcity of anomalies present significant challenges in developing and validating robust anomaly detection systems for machine sounds. To address th…
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…
Distributed collaborative anomalous sound detection by embedding sharing
Kota Dohi, Yohei Kawaguchi
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn a…
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…
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…