The two-dimensional projections produced by the unsupervised models are shown in Fig. . Latent dimensions 1 and 2 are abstract representations generated by the dimensionality reduction techniques that were used. They capture the most significant variance or structural patterns in the data, with each axis representing a combination of the original features that best separates or organizes the data in a lower-dimensional space. The projected waveforms are those of the 'all cells' test set, and were not used in training. Datapoints are coloured according to the cell identity to explicitly examine whether clusters of different cells emerge. It is discovered that this is indeed the case, as individual colours agglomerate together to a large degree in all subplots. This indicates that the distinctiveness of cells in the acoustic dataset dominates over any common links between them and a generic cell state. This distinctiveness appears to be dominant in all models, linear and non-linear, and all feature configurations.