AES Journal Forum

On the Improvement of Localization Accuracy with Non-Individualized HRTF-Based Sounds

Document Thumbnail

Even though individual head-related transfer function (HRTF) filters produce better performance in virtual-reality environments, measuring individuals is labor intensive and expensive. Can training be used to enhance the performance of generic filters? This research shows that short training sessions with feedback allows for perceptual adaptation where simple exposure to generic HRTF filters did not. The benefits of training were observed not only for the trained sounds but also for other stimulus positions that were not part of the training. Apparently, subjects were actually adapting and generalizing to the generic HRTF filters, which is a manifestation of sensory neural plasticity. Learning profiles are unique to individuals. Any testing of localization performance should recognize the influence of training.

JAES Volume 60 Issue 10 pp. 821-830; October 2012
Publication Date:

Click to purchase paper as a non-member or you can login as an AES member to see more options.

No AES members have commented on this paper yet.

Subscribe to this discussion

RSS Feed To be notified of new comments on this paper you can subscribe to this RSS feed. Forum users should login to see additional options.

Start a discussion!

If you would like to start a discussion about this paper and are an AES member then you can login here:

If you are not yet an AES member and have something important to say about this paper then we urge you to join the AES today and make your voice heard. You can join online today by clicking here.

AES - Audio Engineering Society