  Anomalous sound detection (ASD) systems are usually compared by using
threshold-independent performance measures such as AUC-ROC. However, for
practical applications a decision threshold is needed to decide whether a given
test sample is normal or anomalous. Estimating such a threshold is highly
non-trivial in a semi-supervised setting where only normal training samples are
available. In this work, F1-EV a novel threshold-independent performance
measure for ASD systems that also includes the likelihood of estimating a good
decision threshold is proposed and motivated using specific toy examples. In
experimental evaluations, multiple performance measures are evaluated for all
systems submitted to the ASD task of the DCASE Challenge 2023. It is shown that
F1-EV is strongly correlated with AUC-ROC while having a significantly stronger
correlation with the F1-score obtained with estimated and optimal decision
thresholds than AUC-ROC.
