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@@ -22,7 +22,7 @@ Usually, a \ac{CI} system consists out of an external processor with a microphon
\noindent As for any head worn hearing aid, the audio processor of a \ac{CI} system does not only pick up the desired ambient audio signal, but also any sort of interference noises from different sources. This circumstance leads to a decrease in the quality of the final audio signal for the user. Reducing this interference noise through adaptive noise reduction, implemented on a low-power digital signal processor, which can be powered within the electrical limitations of a CI system, is the topic of this master's thesis.
\subsection{Implementation of Adaptive Noise Reduction in Cochlear Implant Systems}
The above problem description of noise interference shows the need of further improvement of \ac{CI} systems in this regard. For persons with a healthy hearing sense, the addition of noise to an observed signal may just mean a decrease in hearing comfort, whereas for aurally impaired people it can make the difference in the basic understanding of information. As everyday environments present fluctuating background noise - from static crowd chatter to sudden sounds of different characteristics — that can severely degrade speech perception, the ability to suppress noise is a crucial benefit for users of cochlear implant systems. \\ \\
\ac{ANR} (also commonly referred as \ac{ANC}), is an advanced signal processing technique that adjusts the parameters of a digital filter to suppress unwanted noise from a signal while preserving the desired target signal. In contrary to static filters (like a high- or low-pass filter), \ac{ANR} uses real-time feedback to adjust said digital filter to adapt to the current circumstances.\\ \\
\ac{ANR} (also commonly referred as \ac{ANC}), is an advanced signal processing technique that adjusts the parameters of a digital filter to suppress unwanted noise from a signal while preserving the desired signal. In contrary to static filters (like a high- or low-pass filter), \ac{ANR} uses real-time feedback to adjust said digital filter to adapt to the current circumstances.\\ \\
The challenge in the implementation of \ac{ANR} in \ac{CI} systems lies in the limited capacities. As the \ac{CI} system is powered by a small battery located in the audio processor, energy efficiency is crucial for a possible solution of the described problem of noise interference. Any approach to a reduction of interference noise must be highly optimized with regard to computing power and implemented on dedicated low-power hardware, being able to be powered within the limitations of a \ac{CI} system.\\ \\
The main topic of this thesis is the optimization of the adaptive filter of the \ac{ANR} algorithm in combination with the used low-power hardware. Its goal is, to deliver the best possible result in interference noise reduction while still being able to be powered by the limited resources of a \ac{CI} system. Different optimization strategies of the adaptive filter algorithm shall be evaluated and compared in regard of their performance and their required computing power.\\ \\
Due to the fact, that the \ac{CI} system is powered by a battery with a relatively small capacity, the firmware is required to work with the least power possible, while maintaining the required performance. Therefore, optimization in regard of a minimization of needed processor clocks is aimed for.