# Automatic Spatial Audio Scene Classification in Binaural Recordings of Music +
## 1 Introduction  
### Background  
Binaural audio technology is rapidly gaining popularity. For example, it is now widely used for the rendering of 360\({}^{\circ}\) virtual reality content in one of the most popular video-sharing Internet services [1]. In their pilot experiment, Research and Development Department of the British Broadcasting Corporation (BBC R&D) has recently produced some of the most prominent classical music concerts in a binaural format [2]. Furthermore, binaural technology is now supported in one of the commonly used web browsers, allowing for real-time rendering of spatial audio scenes [3]. The interested reader is referred to [4] for an overview of the techniques used to record, reproduce, or synthesize binaural signals. Due to the increasing number of applications adopting binaural technology, it is expected that large repositories of binaural recordings will soon be created. This may give rise to challenges concerning semantic search and retrieval of spatial audio content. For example, a hypothetical user of future multimedia systems may search the Internet resources for binaural audio recordings not only according to genre (pop music, jazz, sound effects, etc.) but also with regards to their spatial properties (e.g., audio clips with prominent audio content arriving from above or from behind of the head). According to [PERSON]'s spatial audio scene-based paradigm [5], complex spatial audio scenes could be described using perceptual attributes describing (1) individual audio sources, (2) ensembles of sources, and (3) acoustic environments. However, most of the models, mimicking spatial hearing in humans, are limited to the localization of individual audio sources [6; 7; 8; 9; 10], ignoring higher-level attributes describing complex spatial audio scenes. Early machine-listening algorithms, inspired by spatial hearing in humans, could be characterized as single-source localization models since they were capable of localization of only one audio source at a time [11; 12]. More recently, multiple-source localization models have been developed [6; 7; 8; 9], which constitutes an important step towards quantification of higher-level attributes (e.g., ensemble width), ultimately leading to a holistic characterization of complex spatial audio scenes. While their reported accuracy is deemed to be good, their applicability is limited, as they often require a priori knowledge about the number of sources of interest and their signal characteristics. Another drawback of the multiple-source binaural localization models is that they were developed and tested predominately using speech signals [6; 7; 8; 9]. Hence, their applicability to music recordings is unknown. With the exception of the early models [11; 12], it is now a common practice to employ artificial intelligence-based algorithms in machine-listening systems. They typically include such algorithms as Gaussian mixture models [6] and neural networks [10]. In line with the recent trends in machine learning, deep neural networks and convolutional networks have also been applied to binaural models [7; 8; 13]. It is widely accepted that there are three types of binaural cues responsible for spatial hearing in humans, namely: interaural level difference (ILD), interaural time difference (ITD), and interaural coherence (IC) [14]. While ILD and ITD are predominantly responsible for localization of audio sources in the horizontal plane, IC affects the apparent source width [15]. In addition to binaural cues, spectral cues help humans to localize sources in the sagittal plane [16] and provide help in front-back disambiguation [14]. There are two factors potentially hindering the performance of binaural models, namely a front-back confusion effect [14] and acoustic reverberations (room reflections) combined with background noise.