14-06-2012, 03:52 PM
Hearing Aid System Using Basilar Hardware Membrane Model
[attachment=24451]
Abstract
Our purpose is to apply a basilar hardware
membrane model (BMM) to hearing-aid systems to
solve some of the problem in existing hearing-aid
systems. In this study, we construct a BMM using
Simulink. Also, we experiment on the response
characteristics of our BMM implementation to
C6713DSK, using real sounds and hearing-loss
sounds, which imitate a hearing-impaired person’s
hearing ability.
Introduction
Voice information is very important for
conversation. Generally, a hearing-aid is used in order
to alleviate a hearing impairment. However, more
than half of hearing-aid users feel that existing
hearing-aids are uncomfortable, because users find it
troublesome to have to constantly adjust the hearingaid
due to varying conversation conditions.
In this paper, we construct the BMM using
Simulink in order to apply the BMM to hearing-aid
systems. Also, we experiment with response
characteristics of our BMM implementation to
C6713DSK. Firstly, we show the composition of the
BMM using Simulink. Secondly, we show that our
BMM compare to Johnstone’s data. Lastly, we show
the benefits to hearing-aid by using our BMM
integrated with the C6713DSK when real sounds and
hearing-loss sounds are inputted.
[attachment=24451]
Abstract
Our purpose is to apply a basilar hardware
membrane model (BMM) to hearing-aid systems to
solve some of the problem in existing hearing-aid
systems. In this study, we construct a BMM using
Simulink. Also, we experiment on the response
characteristics of our BMM implementation to
C6713DSK, using real sounds and hearing-loss
sounds, which imitate a hearing-impaired person’s
hearing ability.
Introduction
Voice information is very important for
conversation. Generally, a hearing-aid is used in order
to alleviate a hearing impairment. However, more
than half of hearing-aid users feel that existing
hearing-aids are uncomfortable, because users find it
troublesome to have to constantly adjust the hearingaid
due to varying conversation conditions.
In this paper, we construct the BMM using
Simulink in order to apply the BMM to hearing-aid
systems. Also, we experiment with response
characteristics of our BMM implementation to
C6713DSK. Firstly, we show the composition of the
BMM using Simulink. Secondly, we show that our
BMM compare to Johnstone’s data. Lastly, we show
the benefits to hearing-aid by using our BMM
integrated with the C6713DSK when real sounds and
hearing-loss sounds are inputted.