Set up an acoustic sceneΒΆ

Contemporary auditory models generally work in a feed-forward manner: pre-recorded or pre-generated signals are simply fed into the model and the model calculates and returns the results.

The Two!Ears Auditory Model behaves quite different in that respect - our framework allows to actively explore the provided acoustic scene: the model’s feedback mechanisms could, for instance, initiate a turn of the audio capture unit (dummy head) in order to disambiguate complex input and to arrive at a more precise scenario analysis. Such active exploration can be achieved, on the one hand, in the real world by using a dummy head in order to record binaural ear signals on the fly. Those dummy head should have a moveable head and will be mounted on a robotic platform to allow for translatory movements. For an introduction to that, see Use a robotic platform. On the other hand, this task can be addressed in a virtual way, setting up a simulated acoustic scene in the Binaural simulator. Below, we focus on the latter method.

There are two ways how the Binaural simulator creates the ear signals. One is to specify the entire acoustic scene using meta-data, pass appropriate HRTFs to the simulator, and, with that, render the scenario using the simulator’s Binaural renderer component. The other way is to use a mixture of meta-data and pre-recorded scene details that could be represented by BRIR or BRS files [Horbach1999]. The Binaural room scanning renderer can use this information (BRIRs) to represent sources placed in a room. Stepping further, BRS files enable the simulation of complex scenarios with loudspeaker arrays driven by spatial audio reproduction techniques. With this method the complete synthesised sound field of those reproduction methods could be investigated by binaural synthesis without the need of setting up all the required loudspeakers.


These files can also be used in perceptual experiments with dynamic binaural synthesis (see for example [Wierstorf2014]); here, our model could help to predict the experimental outcome.