| Περίληψη: | The reduced speech intelligibility caused by feed feedback oscillation is a major problem for hearing aid users. The demand for improved signal quality has led researchers to look for feedback reduction techniques. In this Thesis, we studied several feedback reduction schemes
with emphasis in adaptive feedback cancellation algorithms. The main goal was to develop a
system for feedback cancellation that is able to adapt to non-stationary environments while
having reasonable computational complexity. This requirement is imposed by the need to
implement the feedback cancellation scheme in low power DSP systems.
In Chapter 1, we briefly introduced hearing aid systems. We examined the parts that are
made of and the types of hearing aids that are available in the market. Then, we described
the mechanism that causes feedback oscillation in hearing aids and the adverse effects it has on
signal quality.
Chapter 2 contains some theoretical results on the field of adaptive linear system identification
algorithms and simulation results that support this theory. The chapter begins by giving a
derivation of the popular LMS algorithm. A theoretical analysis of LMS using the independence
assumption is also provided. Then we are concerned with the least squares filter. We described
the RLS algorithm and a linear complexity version of it, the FAEST algorithm. Subsequently,
we discussed the FNTF algorithm that trades computational complexity for performance in
solving the system identification problem. Next, we developed a new algorithm, the FLMS, by
making simplifications to FNTF. We also proved that the proposed algorithm outperforms LMS
at least when the input signal is an AR process. Finally, we provided simulation results which
prove the superiority of FLMS over LMS.
Chapter 3 is devoted in using some algorithms described in Chapter 2 for feedback cancellation
in hearing aids. The chapter begins with a hearing aid model that includes an acoustic
feedback mechanism. On this system, a linear filter is added that estimates the acoustic feedback
so that it can be removed from he signal captured by the microphone. The feedback
estimation is performed with LMS and FLMS. Using simulation results, we saw that FLMS can
be successfully used in feedback systems and continues to outperform LMS. We also saw that,
contrary to the open loop case, when feedback is present, the stochastic approximation theory
does not satisfactorily predict the mean learning curves of LMS.
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