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Anomalous sound detection (ASD) systems are usually compared by usingthreshold-independent performance measures such as AUC-ROC. However, forpractical applications a decision threshold is needed to decide whether a giventest sample is normal or anomalous. Estimating such a threshold is highlynon-trivial in a semi-supervised setting where only normal training samples areavailable. In this work, F1-EV a novel threshold-independent performancemeasure for ASD systems that also includes the likelihood of estimating a gooddecision threshold is proposed and motivated using specific toy examples. Inexperimental evaluations, multiple performance measures are evaluated for allsystems submitted to the ASD task of the DCASE Challenge 2023. It is shown thatF1-EV is strongly correlated with AUC-ROC while having a significantly strongercorrelation with the F1-score obtained with estimated and optimal decisionthresholds than AUC-ROC.

F1-EV Score: Measuring the Likelihood of Estimating a Good Decision Threshold for Semi-Supervised Anomaly Detection