A total of 75 FNN models are listed in Table and 18 CNN models in Table . As discussed, all neural networks were trained using the 'all cells' data split and also using the 7 'held out cells' data folds. Additionally, all models were trained with and without dropout. Therefore, a total of 1488 neural network regression models were created, and their test scores will be discussed in the results section. The implementation of dropout was such that 20% of the nodes in each dense layer were randomly switched off at the start of each training epoch. Dropout was not applied to the input and output layers, or to convolutional layers. All training was carried out on UCL's Myriad High Performance Cluster using Graphics Processing Units (GPUs). Each model ran on a single GPU, i.e. no parallelisation was set up. However, multiple GPUs were used to run models concurrently. The cumulative run time for all regression models was approximately 55 days. We note that the models created have a broad range of capacities, from 3 parameters to approximately 2.6 million parameters (Tables and). Used in combination with the seven feature configurations, it is believed that the performance of these models will be highly indicative of the possibilities and limitations of state estimation using acoustic signals.