Performance benchmarks were set by two classical machine learning methods, using config 'A' as well as configs 'B' to 'F'. The first benchmark is Linear Regression; a prototypically simple model. The second benchmark is the SVM; chosen because of its use by Davies et al who claim a generalised state estimator. It should be noted that in SVMs the concept of model parameters is different from neural networks, and it is not defined at model instantiation. Instead, the number of learned parameters is determined during model training. For this reason, Table does not list the number of parameters for SVMs, but those will be shown in the results section for individual models. Feature config 'G' (spectrograms) was used to train CNNs, which are widely used with images. CNNs are capable of identifying structures in two-dimensional datasets by performing transformations to their inputs according to filtering operations. Filters are traversed over the 2D arrays performing a convolution operation, hence the name. Fig. demonstrates at a high level the CNN structure used in this study, which includes two convolutional layers, each succeeded by a max pooling layer. Filters were set to be 3×3 arrays, and these are model parameters that are learned during the training process.