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Master-Vortrag: Perceptual Optimization and Evaluation of a Binaural Signal Modification Algorithm
Mittwoch, 30. November 2022
With individualized binaural signals, it is possible to reproduce auditory scenes such that the signal is perceived similar to the real scene. However, perceptual similarity is no longer achieved when the binaural signal doesn’t fully adapt to diﬀerent listeners and diﬀerent orientations of the listener’s head. To address these problems, a perceptually motivated algorithm referred to as the Binaural Cue Adaptation (BCA) system has been developed at the Institute of Communication Systems. The BCA system is capable of adding both interactivity and individualization to existing binaural signals, thereby achieving a higher degree of perceptual similarity to a corresponding real auditory scene.
In this thesis, a perceptual optimization of the existing BCA system is conducted in that new approaches for some components of the algorithm are proposed, all parametrization options are identiﬁed and the overall best parametrization is chosen. To identify the best parametrization, both an isolated analysis of individual components is conducted and a perceptually motivated optimization procedure for a full system analysis is proposed and implemented.
Finally, a perceptual evaluation based on the result of the perceptual optimization is realized. For this, two listening tests with a total number of 17 participants are conducted – one for a normal and one for a highly reverberant scenario. The results of these listening tests suggest that signals produced by the optimized BCA system achieve a high degree of perceptual plausibility for both reverberation scenarios, with an averaged 2AFC probability to detect a BCA-generated signal of 0.563 for the normal scenario and 0.604 for the highly reverberant scenario.