5 papers · 1 filter
How Much Does Machine Identity Matter in Anomalous Sound Detection at Test Time?
Kevin Wilkinghoff, Keisuke Imoto, Zheng-Hua Tan
Anomalous sound detection (ASD) benchmarks typically assume that the identity of the monitored machine is known at test time and that recordings are evaluated in a machine-wise man…
Context-Aware Query Refinement for Target Sound Extraction: Handling Partially Matched Queries
Ryo Sato, Chiho Haruta, Nobuhiko Hiruma +1
Target sound extraction (TSE) is the task of extracting a target sound specified by a query from an audio mixture. Much prior research has focused on the problem setting under the…
ASDKit: A Toolkit for Comprehensive Evaluation of Anomalous Sound Detection Methods
Takuya Fujimura, Kevin Wilkinghoff, Keisuke Imoto +1
In this paper, we introduce ASDKit, a toolkit for anomalous sound detection (ASD) task. Our aim is to facilitate ASD research by providing an open-source framework that collects an…
Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work
Kevin Wilkinghoff, Takuya Fujimura, Keisuke Imoto +3
When detecting anomalous sounds in complex environments, one of the main difficulties is that trained models must be sensitive to subtle differences in monitored target signals, wh…
Discriminative Neighborhood Smoothing for Generative Anomalous Sound Detection
Takuya Fujimura, Keisuke Imoto, Tomoki Toda
We propose discriminative neighborhood smoothing of generative anomaly scores for anomalous sound detection. While the discriminative approach is known to achieve better performanc…