Classification models were trained using the 'all cells' data split for the purpose of recognising the identity of the cell which produced an acoustic waveform. A small number of simple FNN models, containing a relatively small number of parameters, were trained and tested (Table ). The same training settings and termination strategy as in the regression case were used. Activation functions were again of the ReLU type, except in the output layer where no activation was used. A softmax operation was applied to the model outputs at the point of inference to convert logit vectors to probabilities (logits are the raw, non-normalized predictions). The sparse categorical cross-entropy loss function was used, therefore, labels representing the cell identity were the integers 0 to 6.