Korr 1

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 \include{acronyms}
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 \begin{abstract}
-abstract
+    \noindent The goal of this thesis is the implementation followed by the investigation of improvement options of a real-time capable \ac{ANR} algorithm in \ac{CI} systems. The focus lies on the reduction of the computational load, and subsequently the power consumption, of the used \ac{DSP} core, while still keeping the noise reduction performance as high as possible.\\ \\
+    \noindent The chosen method for noise reduction is the use of a \ac{LMS} algorithm, which is a widely utilized method in this context. The evaluation of the performance is conducted via the \ac{SNR}-Gain, which serves as a metric for the quality of the noise reduction. Several use cases (from simple to realistic) are analyzed to evaluate qualitiy of the output under different conditions.\\ \\
+    \noindent After confirming the general feasibility of the proposed method in a high-level Python-implementation, the algorithm is implemented in C, using \ac{DSP} compiler instrinsic functions to achieve real-time capability. The performance of the C-implementation is then sucessfully compared to the initial high-level implementation, showing only minor deviations.\\ \\
+    \noindent With a working C-implementation in place, a closer look on the achievable performance under full-update settings is taken, which serves as a benchmark-setting for the remaining thesis. The computational cost of the algorithm is evaluated in terms of the needed cycles to compute one audio sample, which can be expressed as a function of the filter length and the update rate.\\ \\
+    \noindent With this formula developed, several noise sources are put under test, to evaluatue the optimal filter length, which is a trade-off between the performance improvement and the computational cost. The ideal filter length is determined at 45 coeffcients, where about 95\% averaged \ac{SNR}-Gain can be achieved.\\ \\
+    \noindent With the filter lenght set, the improvement of the algorithm is tackled, both for a benchmark track and different signal/noise scenarios.\\ \\
+    \noindent The first approach is a reduction of the update rate. This strategy is able to significantly reduce the needed cycles, but with a simultanious considerable decrease in the \ac{SNR}-Gain.\\ \\
+    \noindent The second approach is an error driven optimization, utilizing the idea of a fixed threshold for the error signal, over which the decision over an upgrade of the filter coefficients is made. This approach turns out to be a success, as it is able to achieve a significant reduction in the needed cycles, while only reducing the \ac{SNR}-Gain by a small amount.\\ \\
+    \noindent Therefore, the error driven optimization approach can be seen as the sucessful result of this thesis, as it is able to further improve an already real-time capable \ac{ANR} algorithm by significantly reducing the computational load of the \ac{DSP} core, while only slightly reducing the performance improvement in terms of \ac{SNR}-Gain.\\ \\
+
 \end{abstract}
 \include{chapter_01}
 \include{chapter_02}