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@@ -21,18 +21,9 @@ Usually, a CI system consists out of an external processor with a microphone (``
\label{fig:fig_electrode}
\end{figure}
\noindent As for any head worn hearing aid, the audio processor of a 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{The problem of signal interference in audio processing}
A signal is a physical parameter (e.g. pressure, voltage) changing its value over time. The term "signal interference" describes the overlapping of two or more signals resulting in a new signal. \\ \\A simple example of a desirable signal interference would be the sound generated by playing several strings of a guitar. Hitting one string results in a pure sine wave of a designated frequency (depending on which note is played), perceptible as sound. Hitting a chord (consisting of several strings), the separate sine waves of the strings combine to a new signal through the process of signal interference - in this case a desired, harmonic sound. (see Figure \ref{fig:fig_interference})
\begin{figure}[H]
\centering
\includegraphics[width=0.8\linewidth]{Bilder/fig_interference.png}
\caption{Signal interference of three separate tones resulting in an E-Minor chord.}
\label{fig:fig_interference}
\end{figure}
\noindent In technical environments signal interference is also common when electromagnetic and acoustic noise coexist. Such conditions can cause electromagnetic coupling or broadband acoustic noise that degrades microphone input and digital transmission. Therefore, in auditory applications, signal interference can cause a considerable degradation to the quality of the final signal, posing an additional challenge to aurally impaired people using an implant solution for rehabilitation. Thus, the objective of this thesis shall be the improvement of implant technology in regard of adaptive noise reduction.
\subsection{Implementation of Adaptive Noise Reduction in Cochlear Implant Systems}
The above problem statement of signal interference shows its significance in the improvement of CI systems. 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. \\ \\
Adaptive noise reduction (ANR) (also commonly referred as adaptive noise cancellation (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), ANR uses real-time feedback to adjust said digital filter to adapt to the current circumstances.\\ \\
The above problem description of noise interference shows the need of further improvement of 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. \\ \\
Adaptive noise reduction (ANR) (also commonly referred as adaptive noise cancellation (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), ANR uses real-time feedback to adjust said digital filter to adapt to the current circumstances.\\ \\
The challenge in the implementation of ANR in CI systems lies in the limited capacities. As the 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 CI system.\\ \\
The main solution concept of this thesis is the optimization of the adaptive filter of the 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 CI system. Different variants, like the fully adaptive filter, the hybrid static/adaptive filter and different optimization approaches of the latter one are low-level simulated on the dedicated digital signal processor. Especially, the different optimization strategies of the hybrid static/adaptive filter algorithm shall be evaluated and compared in regard of their required computing power, and therefore, their required power consumption. Depending on the kind of interference noise, the frequency and the intensity, a promising optimization approach is the reduction of adaptation steps per sample while still maintaining an adequate quality of the filtered audio signal.\\ \\
Due to the fact, that the CI system is powered by a battery with a relatively small capacity, the firmware is required to work with the least power possible. Therefore, optimization in regard to a minimization of needed processor clocks is aimed for.