Generalisation is shown to be challenging, and the study proceeds to investigate why. It is shown that very simple classification models can correctly identify which cell produced an acoustic signal, indicating that certain acoustic characteristics are cell-specific. Lastly, a breadth of unsupervised learning techniques is employed to reduce the dimensionality of acoustic signals to two latent dimensions. This aims to investigate the possible emergence of clustering-by-cell in the latent space, which would indicate the prevalence of cell distinctiveness over state-related commonality in the acoustic dataset.