###### Abstract
In addition to these interaural cues, it has been shown that the auditory system makes use of self-motion [7] and the spectral filtering produced by the pinnae to improve localisation accuracy, particularly with regards to elevation and front-back confusion [5; 8]. Given the robustness of the auditory system at performing localisation tasks [5], it should be possible to produce a computational approach using the same auditory cues. Due to the nature of the human auditory system, machine-hearing approaches are often implemented in binaural localisation algorithms, typically using either Gaussian mixture models (GMMs) [9; 10; 11] or neural networks (NNs) [12; 13; 14; 15]. In most cases, the data presented to the machine-hearing algorithm fit into one of two categories: binaural cues (ITD and ILD) or spectral cues. Previous machine-hearing approaches to binaural localisation have shown good results across the training data and, in some cases, good generalisability across unknown data from different datasets [9; 10; 11; 12; 13; 14; 15]. In [14], a cochlear model was used to pre-process head-related impulse responses (HRIRs), the output of which was then used to calculate the ITD and ILD. Two different cochlear models for ITD and ILD calculation were used, as well as feeding the cochlear model output to the NN. The results presented showed that the NN was able to build up a spatial map from raw output of the cochlear model, which performed better under test conditions than using the binaural cues calculated from the output of the cochlea model. [PERSON] et al. [13] used a feature vector comprised of the cross-correlation function and ILD to train their NNs, which were able to produce highly accurate results within the training data. However, upon presenting the NN with unknown data, it was found to have poor generalisation. In [12], [PERSON] et al. presented approaches using a self-organising map and a multi-layer perceptron trained using the ITD and ILD values calculated from a binaural model. They found that both were capable of producing accurate results within the training data, with the self-organisingmap requiring the addition of head rotation to help disambiguate cue similarity between the front and back hemispheres [12]. Their findings suggested that a much larger dataset is required to achieve generalisation with the multi-layer perceptron. In [9; 10; 11], GMMs trained using the ITD and ILD were used to classify the DoA. In both cases, the GMMs were found to produce accurate azimuthal DoA estimates. Their findings showed that the GMM's ability to accurately predict azimuth DoA was affected by the source and receiver distance and the reverberation time, with larger source-receiver distances and reverberation times generally reducing the accuracy of the model [9; 10]. The results presented in [9] showed that a GMM trained with a multi-conditional training (MCT) dataset was able to localise a signal using two different binaural dummy heads with high accuracy. [PERSON] et al. [16] used the supervised binaural mapping technique, to map binaural features to 2D directions, which were then used to localise a sound source's azimuth and elevation position. They presented results displaying the effect of reverberation on prediction accuracy, showing that prediction accuracy decreased as reverberation times increased. They additionally showed that the use of a binaural dereverberation technique improved prediction accuracy across all reverberation times [16]. Recent work by [PERSON] et al. [15] compared the use of GMM and deep NNs (DNNs) for the azimuthal DoA estimation task. The DNN made use of head rotation produced by a KEMAR unit (KEMAR: Knowles Electronics Manikin for Acoustic Research) is a head and torso simulator designed specifically for, and commonly used in, binaural acoustic research) [17] fitted with a motorised head.