## 1 Introduction
Depending on the synthesis approach, the room geometry, source position, and the walkable area have to be defined. Most of the existing  
Figure 1: Basic block diagram of a position-dynamic binaural synthesis system showing different functional blocks with examples of realization. Bold marked realizations are related to the approaches presented. systems and evaluations thereof are limited to simple room geometries like shoe-box rooms (for example, in [5; 9]). Furthermore, the walkable areas are often placed centrally in the room, presumably to avoid special acoustic effects when being close to walls or corners. However, these restrictions are not necessarily system-related. As this research field is still emerging, more complex room acoustics simply have not been evaluated yet. Depending on the real-time capability of the succeeding filter creation step, a fine or coarse sampling of the walking area has to be considered. Sub-sampling the area based on psychoacoustic assumptions, for example, by considering just noticeable differences (JND) for direction and distance changes [10; 11], can avoid the effort for a continuous adaptation of filter coefficients. In this paper, we will discuss the Maximum Allowed Error Method (MAEM) as one possibility to sample the walkable area [12]. When the listener changes his/her pose or position new filters need to be computed (or loaded). The most flexible solution would be a parametric filter creation approach. These are usually based on a measured or assumed model of a room or scene [5; 13; 14]. The room impulse response is decomposed into modifiable parameters which can be changed depending on the listener movement. This allows an efficient adaptation of filter coefficients, but the success relies on the quality of the model. Filter shaping approaches rely on a BRIR measurement on one position, and only certain properties of filters are adapted when the listener moves such as energy decay curve (EDC), level of direct sound, or initial time delay gap (ITDG) [15; 16; 17]. Often these changes are empirically determined, but they also can be estimated by simple models (such as inverse square law). These algorithms will be discussed in Section 2.2. The idea of these approaches are quite similar to ambisonics-based approaches. Auralization for different listener positions are realized by transformations based on simple models, e.g., distance-dependent attenuation or angular attenuation to mimic the directivity of sound sources [9; 18]. The last step is the real-time rendering. A common solution for ambisonics-based approaches is the rendering of a virtual spherical loudspeaker setup [8; 18]. The ambisonics audio signals are then converted to binaural signals. This is done by assigning an HRTF to each virtual loudspeaker. During the real-time rendering, only the gains of the virtual speakers are adjusted and HRTFs have to be applied. Ambisonics-based approaches are especially suited for realizing 6-DoF experience on the basis of recorded sound fields. Other approaches (especially the ones mentioned here) usually deliver binaural room impulse responses. These need to be convolved with the desired audio signals. The filters have to be changed each time the position or the pose of the listener changes. As it has become clear, the plausible creation of virtual audio objects fused into a real room is the main challenge for AAR. To recognize audio objects, the human brain acts as a great pattern recognizer comparing learned and thus expected audio cues with the ones from the real surrounding. Additionally, presented audio objects have to fit these expectations [19; 20]. A too large acoustic deviation between the acoustic properties of the real space and the virtually reproduced environment leads to a cognitive mismatch and thus to a collapse in the plausibility of the overall auditory scene. In the context of binaural synthesis, we call this the room divergence effect. The most prominent listening impression in this case is a collapse of the externalization of auditory events [21; 22].