{"id":371261,"date":"2017-03-15T18:18:59","date_gmt":"2017-03-16T01:18:59","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=371261"},"modified":"2018-10-26T14:41:10","modified_gmt":"2018-10-26T21:41:10","slug":"interaural-time-delay-personalisation-using-incomplete-head-scans","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/interaural-time-delay-personalisation-using-incomplete-head-scans\/","title":{"rendered":"Interaural time delay personalisation using incomplete head scans"},"content":{"rendered":"

When using a set of generic head-related transfer functions (HRTFs) for spatial sound rendering, personalisation can be considered to minimise localisation errors. This typically involves tuning the characteristics of the HRTFs or a parametric model according to the listener\u2019s anthropometry. However, measuring anthropometric features directly remains a challenge in practical applications, and the mapping between anthropometric and acoustic features is an open research problem. Here we propose matching a face template to a listener\u2019s head scan or depth image to extract anthropometric information. The deformation of the template is used to personalise the interaural time differences (ITDs) of a generic HRTF set. The proposed method is shown to outperform reference methods when used with high-resolution 3-D scans. Experiments with single-frame depth images indicate that the method is applicable to lower resolution or partial scans which are quicker and easier to obtain than full 3-D scans. These results suggest that the proposed method may be a viable option for ITD personalisation in practical applications.<\/p>\n","protected":false},"excerpt":{"rendered":"

When using a set of generic head-related transfer functions (HRTFs) for spatial sound rendering, personalisation can be considered to minimise localisation errors. This typically involves tuning the characteristics of the HRTFs or a parametric model according to the listener\u2019s anthropometry. However, measuring anthropometric features directly remains a challenge in practical applications, and the mapping between 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