On the adaptation to non-individualized HRTF auralisations: A longitudinal study

Abstract

Auralisations with HRTFs are an innovative tool for the reproduction of acoustic space. Their broad applicability depends on the use of non-individualised models, but little is known on how humans adapt to these sounds. Previous findings have shown that simple exposure to non-individualised virtual sounds did not provide a quick adaptation, but that training and feedback would boost this process. Here, we were interested in analyzing the long-term effect of such training-based adaptation. We trained listeners in azimuth and elevation discrimination in two separate experiments and retested them immediately, one hour, one day, one week and one month after. Results revealed that, with active learning and feedback, all participants lowered their localization errors. This benefit was still found one month after training. Interestingly, participants who had trained previously with elevations were better in azimuth localization and vice-versa. Our findings suggest that humans adapt easily to new anatomically shaped spectral cues and they are able to transfer that adaptation to non-trained sounds.