18 citations · 18 across the 11 of their papers we have counts for
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Self-Supervised Learning for Anomalous Sound Detection
Kevin Wilkinghoff
State-of-the-art anomalous sound detection (ASD) systems are often trained by using an auxiliary classification task to learn an embedding space. Doing so enables the system to lea…
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…
Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?
Kevin Wilkinghoff, Frank Kurth
State-of-the-art anomalous sound detection systems often utilize angular margin losses to learn suitable representations of acoustic data using an auxiliary task, which usually is…
TACos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping
Kevin Wilkinghoff, Alessia Cornaggia-Urrigshardt
To segment a signal into blocks to be analyzed, few-shot keyword spotting (KWS) systems often utilize a sliding window of fixed size. Because of the varying lengths of different ke…