8 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…
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…
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…
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…
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…
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…
F1-EV Score: Measuring the Likelihood of Estimating a Good Decision Threshold for Semi-Supervised Anomaly Detection
Kevin Wilkinghoff, Keisuke Imoto
Anomalous sound detection (ASD) systems are usually compared by using threshold-independent performance measures such as AUC-ROC. However, for practical applications a decision thr…