## 1 Introduction  
Since machine listening became an eminent field in the early 1990s, the vast majority of research has focused on automatic speech recognition (ASR) [1] and computational solutions to the well-known 'cocktail party problem'--the \"ability to listen to and follow one speaker in the presence of others\" [2]. This is now a mature field of study, with robust speech recognition systems featured in most modern smartphones. There has also been a great deal of research on music information retrieval (MIR) [3], a technology with applications in intelligent playlist algorithms used by online music streaming services [4]. There has been comparatively little research investigating the automatic recognition of general acoustic scenes or acoustic events, though there has been an increase in interest in this area in recent years, largely due to the annual Detection and Classification of Acoustic Scenes and Events (DCASE) challenges established in 2013 [5]. The DCASE challenges have attracted a large number of submissions designed to solve the problem of acoustic scene classification (ASC) or acoustic event detection (AED). A typical ASC or AED system requires a feature extraction stage in order to reduce the complexity of the data to be classified. The key is the coarsening of the available data such that similar sounds will yield similar features (generalisation), yet the features should be distinguishable from those yielded by different types of sounds (discrimination). Generally, the audio is split into frames and some kind of mathematical transform is applied in order to extract a feature vector from each frame. Features extracted from labelled recordings (training data) are used to train some form of classification algorithm, which can then be used to return labels for new unlabelled recordings (testing data). See [6] for a thorough overview of this process. The systems submitted to DCASE all identify acoustic scenes and events based upon features extracted from monaural or stereophonic recordings. A small number of systems have used spatial features extracted from binaural recordings [7; 8; 9], but the potential for extracting features using more sophisticated spatial recordings remains almost completely unexplored. This is due to a number of factors, including inheritance of techniques from ASR and MIR and the envisioned applications of ASC and AED. A majority of the early research into ASR approached the problem with the aim of emulating elements of human sound perception. This \"biologically relevant\" [1] approach can be seen in the popular Mel-frequency cepstral coefficient (MFCC) features, which use a mel-scaled filter bank in order to crudely emulate the human cochlear response [10]. A more fundamental self-imposed limitation of this approach is the use of one- or two-microphone recordings. Although, on introducing the DCASE challenge, [PERSON] et al. stated that \"human-centric aims do not directly reflect our goal... which is to develop systems that can extract semantic information about the environment around them from audio data\" [5], it is natural to inherit techniques from more mature related fields. The most commonly stated applications of ASC and AED technologies include adding context-awareness to smart devices, wearable technology, or robotics [6] where mounting of spatial microphone arrays would perhaps be more impractical. Another application is automatic labelling of archive audio, where the majority of recordings will be in mono or stereo format [5; 6]. Some lesser-considered applications of ASC and AED technology involve the holistic analysis of acoustic scenes in and of themselves. The focus here is gaining a greater understanding of the constituent parts of acoustic scenes and how they change over time. This has potential applications in acoustic ecology research for natural environments, re-synthesis of acoustic scenes for virtual reality, and in obtaining more detailed measures for urban environmental sound than the prevailing \(L_{Aeq}\) sound level metric.