6 papers
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…
SONAR: Self-Distilled Continual Pre-training for Domain Adaptive Audio Representation
Yizhou Zhang, Yuan Gao, Wangjin Zhou +3
Self-supervised learning (SSL) on large-scale datasets like AudioSet has become the dominant paradigm for audio representation learning. While the continuous influx of new, unlabel…
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…
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…
Trainingless Adaptation of Pretrained Models for Environmental Sound Classification
Noriyuki Tonami, Wataru Kohno, Keisuke Imoto +4
Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or…