# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 2 Materials and Methods
### Method Overview  
A traditional two-stage approach of manually designed feature extractor followed by a machine learning-based classifier was adopted in this study, as illustrated in Figure 1. Note that in the proposed topology no source separation algorithms, or any other sophisticated algorithms aiming to isolate and localize audio sources prior to their classification, were employed. Moreover, for the reasons explained earlier, it was decided to develop a method without simulating any head rotation effects, which further simplified the development. It was assumed by these authors that the proposed two-stage algorithm, simulating a stationary head scenario, would be adequate for the task of classifying spatial audio scenes. Some of the recently developed methods for localization of audio sources in complex binaural recordings require a priori knowledge of a number of audio objects present in a spatial scene (e.g., [6]). No such requirement was imposed on this method. As it is shown in the remainder of the paper, the proposed method was successfully tested using binaural recordings with the number of audio sources ranging from 5 to 42. The three basic spatial audio scenes were considered in this study, as overviewed in Table 1. They differed with respect to a horizontal distribution of foreground and background audio content. Foreground sound objects represented easily identifiable, important, and clearly perceived audio sources, whereas background objects normally represented reverberant, unimportant, unclear, ambient, \"foggy\", and distant sound sources. The first scene, referred to as foreground-background (_FB_), described a scenario where a listener perceived predominantly foreground audio content as arriving from the front and background audio content as originating from the back. It may represent a typical situation of a listener sitting in the audience and listening to the musicians located at the stage, e.g., during a classical music concert. By contrast, background-foreground (_BF_) scenes represented a \"reversed\" scenario, where background audio content arrived from the front and predominantly foreground audio content was perceived from the back (behind the head). This scene could be encountered in virtual reality applications, including computer games. The third scene, termed foreground-foreground (_FF_), described a situation where predominantly foreground audio content was perceived by a listener as arriving both from the front and from the back. It could be exemplified by a situation where a listener was surrounded by musicians. This scene could be often encountered in binaural pop music recordings. The above categorization of spatial audio scenes was originally inspired by [PERSON]'s spatial audio scene-based paradigm [PERSON] (1980). In a form limited to two categories (_FB_, _FF_), it was used by [PERSON] et al. [PERSON] et al. (1988) to label experimental recordings of classical music. A similar categorization of spatial scenes, although with a different nomenclature (\"stage-audience recording scenario\" versus \"360 deg stage scenario\"), was also adopted by [PERSON] and [PERSON] [PERSON] and [PERSON] (2008) in their experiment investigating 3D audio microphone techniques. Such categorization also proved to be useful in explaining empirical data acquired from the experiments with spatial sound [PERSON] and [PERSON] (2008). As already mentioned in the Introduction, a performance of audio quality expert systems could be improved if information regarding a category of the basic spatial scene (_FB_ or _FF_) was provided to their input [PERSON] and [PERSON] (2008). In addition to the three categories of spatial audio scenes, already overviewed in Table 1, an extra (fourth) category was also used in our previous study [PERSON] and [PERSON] (2008). It was signified as background-background (_BB_) and referred to a situation where a listener was surrounded solely by background audio objects. Such category was particularly relevant in the case of environmental recordings.