10 citations · 10 across the 8 of their papers we have counts for
8 papers
Automatic Inspection Based on Switch Sounds of Electric Point Machines
Ayano Shibata, Toshiki Gunji, Mitsuaki Tsuda +4
Since 2018, East Japan Railway Company and Hitachi, Ltd. have been working to replace human inspections with IoT-based monitoring. The purpose is Labor-saving required for equipmen…
Retrieving Time-Series Differences Using Natural Language Queries
Kota Dohi, Tomoya Nishida, Harsh Purohit +2
Effectively searching time-series data is essential for system analysis; however, traditional methods often require domain expertise to define search criteria. Recent advancements…
Timbre Difference Capturing in Anomalous Sound Detection
Tomoya Nishida, Harsh Purohit, Kota Dohi +2
This paper proposes a framework of explaining anomalous machine sounds in the context of anomalous sound detection~(ASD). While ASD has been extensively explored, identifying how a…
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…
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 (…
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…