The accuracy of classification models at predicting the cell identity (7 classes), on test data from 'all cells' (13,399 samples), is shown in Fig. . Perfect classification accuracy, equal to 1, was achieved by models with a relatively small number of parameters, even when using simple feature configurations such as 'B' and 'C'. The ease of the classification task suggests that acoustic signals obtained from different cells contain some distinctly different characteristics. This in itself does not guarantee the absence of other acoustic characteristics which could link multiple cells to a common cell state. Nevertheless, it reinforces the hypothesis that the success of regression models on this dataset is likely due to the identification of cell-specific patterns.