## 1 Introduction
The \(L_{Aeq}\) measurement aggregates all sound present in a scene into one single sound level figure. This disregards the variety of sources of the sounds, influencing much environmental sound legislation to focus on its suppression--an \"environmental noise approach\" [11]. A machine listening system could consider the content of an acoustic scene as well as absolute sound levels. This information could be used to create more subtle metrics regarding urban sound, taking into account human perception and preference--a \"soundscape approach\" [11]. This kind of system was proposed by [PERSON] et al. [12], but despite some promising work involving source separation in Ambisonic audio [13], published results from that project have been limited. The term _soundscape_ is used here according to the ISO definition, meaning \"the acoustic environment of a place, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors\" [14]. This emphasis on perception is apt in this case, but a subjective perceptual construct is clearly not what a machine listening system will receive as input for analysis. We therefore use the term 'acoustic scene' when discussing recordings. Another potential application of such a system is assisting in the synthesis of acoustic scenes for experimental purposes. If a researcher or organisation wishes to obtain detailed data on human perception of environmental sound, one technique that can be used is a sound walk, in which listening tests can be conducted in situ at a location of interest. This gives the most realistic stimulus possible, direct from the environment itself. Results gained using this technique are therefore as representative as possible of subjects' reactions with respect to the real-world acoustic environment, a factor known as 'ecological validity' [15; 16]. The key disadvantages are that this method is not repeatable [15] and can be very time-consuming [17]. An alternative is laboratory reproduction of acoustic scenes, presented either binaurally [18] or using Ambisonics [19; 20]. These are less time-consuming and more repeatable [15], but the clear disadvantage is the potential for reduced ecological validity of the results,which leads to the criticism that lab results \"ought to be validated in situ\" [21]. A key issue is how to condense an urban sound recording into a shorter format whilst retaining ecological validity. Methods for this have included selection of small clips at random [15] or manually arranging a acoustic scene \"composition\" in order to \"create a balanced impression\" [19]--essentially condensing the acoustic scene by ear. Whilst manual composition of a stimulus is undoubtably more robust than presentation of a random short clip that may or may not be representative of the acoustic scene as a whole, it is not an optimal process. The subjective recomposition of an acoustic scene by a researcher introduces a source of bias that could be reflected in the results. A machine listening system could effectively bypass this issue by providing detailed analysis that could assist with synthesis of shorter clips that remained statistically representative of real acoustic scenes. The limitation to low channel counts is less applicable given these applications of machine listening technology. Spatial recordings offer the potential for a rich new source of information that could be utilised by machine listening systems and higher channel counts offer the opportunity for sophisticated source separation [13; 22] which could assist with event detection. The lack of research into classification using spatial audio features could also be due to the fact that there has been, as yet, no comprehensive database of spatially-recorded acoustic scenes. Any modern database of recordings intended for use in ASC research must contain many examples of each location class.