## 1 Introduction  
Following its success in virtual-reality applications [1; 2; 3], binaural technology is now being gradually adopted by professional and amateur broadcasters [4; 5], with a prospect of becoming a prominent \"tool\" for delivering 3D audio content over the Internet. Binaural technology was recently introduced to one of the most popular video sharing services [6]. Consequently, one may expect that large repositories of spatial audio recordings, stored in a binaural format, will be created soon. This will inevitably give rise to new challenges related to the search and retrieval of \"spatial audio information\" from such recordings. There are several reasons for which the research into the automatic spatial audio scene classification in binaural recordings is important. The outcomes of such research could help to design systems for the semantic search and retrieval of audio content in binaural recordings based on spatial information, allowing listeners to explore Internet resources looking for recordings (or their selected excerpts) with a music ensemble located at particular directions, e.g., behind the head of a listener. Moreover,the algorithm for the automatic spatial audio scene classification can be used as a building block of spatial audio quality prediction systems (the performance of spatial quality prediction systems can be improved by providing them with information on spatial audio scene characteristics [7]). Additionally, in the authors' opinion, the automatic spatial scene classifiers could facilitate the operation of the audio format conversion algorithms used for the up-mixing of binaural signals to multichannel loudspeaker signals. Furthermore, they might be exploited as an automatic preselection tool for gathering audio material for subjective audio quality assessment tests. While the application context in this paper is limited to the domain of reproduced audio, the algorithms for the automatic spatial scene recognition could also be embedded in environment-aware binaural hearing aids, adapting their parameters depending on the surrounding audio scenes. Most of the research within the area of machine listening of binaural audio recordings has been focused on the localization of individual audio sources [8; 9], ignoring higher-level descriptors of complex spatial audio scenes. This study belongs to an emerging field of research aiming to develop the machine learning methods for the holistic characterization of spatial audio scenes for reproduced sound [10; 11]. It builds on our previous work on the automatic spatial audio scene classification of the binaural recordings using traditional machine learning methods [12; 13] and on our recent pilot study employing a deep learning approach [14]. The aim of this work is to compare the performance of human listeners against the performance of the modern machine learning algorithms in the task of the classification of the basic spatial audio scenes in synthetically generated but practical binaural recordings of music. The rationale for undertaking this topic comes from the observation that with the advent of modern machine learning algorithms, most notably deep learning techniques, for some classifications tasks, such as image recognition, the performance level of the computational algorithms matched or even exceeded that exhibited by humans [15]. Therefore, it was interesting to find out how this human-machine comparison would turn out in the case of this new field of research. The following three scenes were considered in the study: (1) music ensemble (a group of musical sound sources) located in the front of a listener, (2) music ensemble located at the back of a listener, and (3) music ensemble distributed around a listener in the horizontal plane (scenes encompassing a height dimension were left for future work). In this study, 212 listeners were recruited to classify the binaural audio excerpts according to the aforementioned three spatial scenes.