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With the virtual environment developed here, the characteristic
sound radiation patterns of musical instruments can be experienced
in real-time. The user may freely move around a musical instrument, thereby receiving acoustic and visual feedback in real-time. The perception of auditory and visual effects is intensified by the combination of acoustic and visual elements, as well as the option of user interaction. The simulation of characteristic sound radiation patterns is based on interpolating the intensities of a multichannel recording and offers a near-natural mapping of the sound radiation patterns. Additionally, a simple filter has been developed, enabling the qualitative simulation of an instrument’s characteristic sound radiation patterns to be easily implemented within real-time 3D applications. Both methods of simulating sound radiation patterns have been evaluated for a saxophone with respect to their functionality and validity by means of spectral analysis and an auditory experiment.
Using spatial audio successfully for augmented reality (AR) applications is a challenge, but is awarded with an improved user experience. Thus, we have extended the AR/VR framework \sc Morgan with spatial audio to improve users orientation in an AR application. In this paper, we investigate the users’ capability to localize and memorize spatial sounds (registered with virtual or real objects). We discuss two scenarios. In the first scenario, the user localizes only sound sources and in the second scenario the user memorizes the location of audio-visual objects. Our results reflect spatial audio performance within the application domain and show which technology pitfalls still exist. Finally, we provide design recommendations for spatial audio AR environments.
Virtual TV studios use actor tracking systems for resolving the occlusion of computer graphics and studio camera image. The actor tracking delivers the distance between actor and studio camera. We deploy a photonic mixing device, which captures a depth map and a luminance image at low resolution. The renderer engines gets one depth value per actor using the OSC protocol. We describe the actor recognition algorithm based on the luminance image and the depth value calculation. We discuss technical issues like noise and calibration.