Abstract: Anomaly-detection methods based on classification confidence are applied to the DCASE 2020 Task 2 Challenge on Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The final systems for submitting to the challenge are ensembles of two classification-based detectors. Both classifiers are trained with either known or generated properties of normal sounds as labels: one is a model to classify sounds into machine type and ID; the other is a model to classify transformed sounds into data-augmentation type. As for the latter model, the normal sound is augmented by using sound-transformation techniques such as pitch shifting, and data-augmentation type is used as a label. For both classifiers, classification confidence is used as the normality score for an input sample at runtime. An ensemble of these approaches is created by using probability aggregation of their anomaly scores. The experimental results on AUC show superior performance by each detector in relation to the baseline provided by the DCASE organizer. Moreover, the proposed ensemble of two detectors generally shows further improvement on the anomaly detection performance. The proposed anomaly-detection system was ranked fourth in the team ranking according to the metrics of the DCASE Challenge, and it achieves 90.93% in terms of average of AUC and pAUC scores for all the machine types, and that score is the highest of those scores achieved by all of the submitted systems.