About This Project

Accurate reproduction of acoustic environments is important in hearing-aid research. Researchers have created special digital filters to achieve 3D localization for hearing-aids. However, there are two major issues that are not addressed using these filters: 1) hearing aids do not address the “cocktail party effect” problem, and 2) these filters are anatomically dependent and, hence, are known to cause localization errors. It is proposed to investigate techniques to address these problems.

Ask the Scientists

What is the context of this research?

The accurate reproduction of acoustic real-world environments is becoming of increasing importance in hearing device research and development. Researchers have measured extensive sets of special digital filters called generic HRTFs with pairs of Behind-The-Ear (BTE) hearing aids using KEMAR mannequins. However, there are two major issues that are not addressed using these generic HRTFs: 1) BTE hearing aids do not address the “cocktail party effect” problem (i.e., our ability to focus our attention on a particular stimulus while filtering out a range of other stimuli), and 2) generic HRTFs are anatomically dependent and, hence, are known to cause localization errors.

What is the significance of this project?

If successful, this research will improve BTE hearing-aid performance which will effect a significant portion of the population. According to the NIH, an estimated 17% of American adults report having some form of hearing loss and nearly half of adults ages 75 years and older have hearing loss. Additionally, the work resulting from this project will serve as the basis for other grant submissions.

What are the goals of the project?

It is proposed to start investigating techniques to help BTE hearing-aid users with the “cocktail party effect” problem. Additionally, creation of an interface will be explored which allows the BTE hearing aid user to control source levels and positioning of the sound sources. Lastly, a customizable HRTF model will be investigated that, if successful, will generate HRTFs that are closely related to the anatomical features of the user which is expected to reduce localization errors significantly.