## 2 Proposal of a Position-Dynamic Binaural Synthesis System
### Real-Time Processing  
Real-time convolution for binaural synthesis requires low delay in order to keep the overall system latency (SL) below perceptual thresholds. When the latency is above a certain threshold, static sound sources will no longer be stable when the listener moves his head. Therefore, high SL can be a cue for listeners to detect whether a sound source is real or virtual. For dynamic synthesis considering head rotation, [PERSON] [34] mentions a threshold for SL around 100 ms. The measured thresholds depend on the signal and the test paradigm which is used for testing. For pose and position dynamic systems in AR, additional parameters become evident: Typically, visual cues are available as reference for the virtual sound sources which could lower the SL threshold depending on the positional and temporal precision of the visual cue in terms of a temporal and spatial Ventriloquism-effect [35]. However, the presence of a visual object may increase sensitivity to latency effects, as matching can always take place especially in an AR scenario. When the listener is able to change his position in addition to head movements, higher movement velocity and acceleration are expected in comparison to head movements only. No studies could be found on this subject. In case of BRIR rendering, long filters have to be convolved with the source signal in real-time for several sound sources at the same time. The state-of-the-art solution for this use case is a blocked convolution (overlap-add or overlap-save) with uniformly partitioned filters. This solution is significantly faster than using non-partitioned filters, but the computational complexity increases linearly with increased filter size and decreased block size. In case of limited computational power, a compromise between filter length and block size (which directly relates to the delay induced by the convolution) has to be found. To overcome this limitation, filters can be partitioned nonuniformly (e.g., short segments for the direct sound and early reflections and long segments for the late reverb). The drawback of this solution is the implementation effort, because each sub-convolution needs to be scheduled correctly. This may require fine tuning to a specific hardware configuration [36]. Another way to reduce the computational load is to make use of the perceptual mixing time [31]. Depending on the room volume, this value can range between 30 to 100  ms [31]. It has to be noted that these values were only evaluated for a change of the head orientation but not position. This means that only parts of the filter have to be exchanged when the listener moves around or changes his head pose. Even though this reduces the amount of data which needs to exchanged, the load for the convolution itself remains the same. [PERSON] and [PERSON] [37] conducted a small study considering different source and receiver positions in the room. For this specific room and these positions, they mention a time frame of 40 to 60 ms after which the BRIRs could be exchanged without perceptual consequences. However, the listening positions were always located in a close distance in front of the (virtual) loudspeaker. In 6-DOF scenarios, listeners can also walk to positions  
Figure 9: Deviation of broadband DRR between synthesized and measured BRIRs for incidence angle of 30\({}^{\circ}\). Left the results for BRIRs without SSD consideration, and right with SSD consideration are shown. The Black dot represents the measured position for the synthesis “D1”, active source “S1” is placed 1 m below of position “A2”. with very low direct sound energy. In such cases, listeners may be more sensitive to small changes in the room acoustics. When BRIRs are not precomputed, additional processing power is needed to synthesize or simulate them on-the-fly. While real-time capability depends on the specific algorithm in the first place, some techniques to save processing power can be applied to any algorithm. One of these techniques could be the aforementioned Maximum Allowed Error Method.