## 1 Introduction  
While binaural audio technology has been known for decades [1], advancements in consumer electronics facilitated its widespread adoption predominantly during the post-millennial era. Nowadays, it is not only used in virtual reality applications and video games, but also supported by music and video streaming services [2]. Consequently, one can anticipate that large repositories of binaural audio recordings will be created soon, which will give rise to new challenges with the search and retrieval of \"spatial audio information\" from such recordings. Building on [PERSON]'s spatial audio scene-based paradigm [3], complex spatial audio scenes can be described at the three following hierarchical levels: (1) low level of individual audio sources, (2) mid-level of ensembles of sources, and (3) high level of acoustical environments. However, the state-of-the-art computational models for binaural localization developed so far were intended to localize individual audio sources [4; 5; 6; 7; 8; 9] rather than to characterize complex spatial audio scenes at various descriptive levels (see [10] for the review of the binaural localization models). They were designed using predominantly speech signals and were intended to localize speakers [7]. Attempts to apply computational models for binaural localization to \"music\" audio signals are very rare [11; 12]. Likewise,there are only a few developments [13] aiming to characterize complex spatial audio scenes at the mid- or high level, using the hierarchical paradigm described above [3]. Moreover, most of the binaural localization models developed so far are constrained to 2D localization in the horizontal plane [4; 5; 6; 7; 8; 9]. Some preliminary models allowing for full-sphere binaural sound source localization have been proposed, only recently [14; 15]. The goal of this study was to develop a method for the automatic localization of music ensembles using binaural signals, assuming that the ensembles are positioned in one of the following four locations with respect to the listener: front, back, up, and down (a music ensemble is understood as a group of musical sound sources, such as instruments or singers). It must be emphasized that the above task is not trivial, given the front-back and/or up-down confusion that might occur with human listeners in non-head-tracked binaural audio reproduction [16; 17]. The idea of using the recordings exhibiting unusual placements of music ensembles may initially appear questionable, considering that in traditional music recordings, ensembles are typically positioned in front of a listener. However, since the state-of-the-art binaural systems enable sound engineers to \"pan\" foreground audio content behind, above, or below a listener, modern binaural recordings often involve unusual spatial scene arrangements. In this work, the method employed to localize the ensembles was based on the convolutional neural network (CNN). In general, the proposed method could be employed in future systems intended for \"spatial scene analysis\" in binaural signals. In particular, it may facilitate the next generation of Internet search algorithms allowing their users to retrieve binaural music recordings exhibiting given spatial characteristics. It could also be used as a part of audio up-mixing algorithms (e.g., converting binaural signals to 22.2 audio format), providing such algorithms with information about the location of music ensembles. Furthermore, the proposed method could be employed as a part of an objective spatial audio quality assessment algorithm because of the reported interaction between spatial location of foreground audio objects and the perceived audio quality [18]. Additionally, it may help disambiguate front audio sources from back ones in algorithms localizing individual audio sources in binaural signals [19]. This study builds on our prior work.