###### Abstract  
Spatial impulse response analysis techniques are commonly used in the field of acoustics, as they help to characterise the interaction of sound with an enclosed environment. This paper presents a novel approach for spatial analyses of binaural impulse responses, using a binaural model fronted neural network. The proposed method uses binaural cues utilised by the human auditory system, which are mapped by the neural network to the azimuth direction of arrival classes. A cascade-correlation neural network was trained using a multi-conditional training dataset of head-related impulse responses with added noise. The neural network is tested using a set of binaural impulse responses captured using two dummy head microphones in an anechoic chamber, with a reflective boundary positioned to produce a reflection with a known direction of arrival. Results showed that the neural network was generalisable for the direct sound of the binaural room impulse responses for both dummy head microphones. However, it was found to be less accurate at predicting the direction of arrival of the reflections. The work indicates the potential of using such an algorithm for the spatial analysis of binaural impulse responses, while indicating where the method applied needs to be made more robust for more general application. machine-hearing; machine-learning; binaural room impulse response; spatial analysis; direction of arrival 2018 8 105 2017 2018  
applications, including: music production, game sound design, alongside other audio-visual media. In acoustics, the spatiotemporal characteristics of reflections arising from sound propagation and interaction within a given bounded space can be captured through measuring the room impulse response for a given source/receiver pair. One problem associated with this form of analysis is obtaining a prediction for the direction of arrival (DoA) of these reflections. Understanding the DoA of reflections can allow for the formulation of reflection backpropagation and geometric inference algorithms, amongst other features, that reveal the properties of the given acoustic environment for which the impulse response was obtained. This has applications in robot audition, sound source localisation tasks, as well as room acoustic analysis, treatment and simulation. These algorithms can be used to develop an understanding of signal propagation in a room, allowing the point of origin for acoustic events arriving at the receiver to be found. This knowledge of the signal propagation in the environment can then be used to acoustically treat the environment, improving the perceptibility of signals produced within the environment. Conversely, the inferred geometry can be used to simulate the acoustic response of the room to a different source and receiver through the use of computational acoustic simulation techniques. Existing methods [1; 2; 3] have approached reflection DoA estimation using four or more channels, while methods looking at localising the components in two-channel BRIRs have generally shown poor accuracy for predicting the DoA of the reflections in these BRIRs [4]. This paper investigates a novel approach to using neural networks for DoA estimation for the direct and reflected sound components in BRIRs. The reduction in the number of channels available for analyses significantly adds to the complexity of extracting highly accurate direction of arrival predictions. The human auditory system is a complex, but robust system, capable of undertaking sound localisation tasks under varying conditions with relative ease [5]. The binaural nature of the auditory system leads to two main interaural localisation cues: interaural time difference (ITD), the time of arrival difference between the signals arriving at the two ears, and interaural level difference (ILD), the frequency-dependent difference in signal loudness at the two ears due to the difference in propagation path and acoustic shadowing produced by the head [5; 6].