2.3

chapter_01.tex

@@ -36,7 +36,6 @@ Thus, the objective of this thesis shall be the improvement of implant technolog
 \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 concept of ANR was already proposed in the 1930’s by the German Physician Paul Lueg in form of two antiphase signals cancelling each other, but the fundamental Least-Mean-Square algorithm to realize a technical implementation of ANR itself was developed not until towards the end of the 1950s. The first commercial use of ANR was finally seen in form of an aviation headset for pilots in 1989.\\ \\
 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 DSP. 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.

chapter_02.tex

@@ -81,8 +81,19 @@ Figure \ref{fig:fig_iir} visualizes a simple IIR-filter with one feedforward coe
 \end{figure}
 
 \subsection{Introduction to Adaptive Nose Reduction}
-
+As already mentioned in the introduction, environmental noise severely degrades cochlear implant user's speech understanding and listening comfort. The traditional concept of static noise reduction, such as fixed filters, are not a feasible solution due to dynamic acoustic conditions where the type, intensity, and spectral composition of noise can change rapidly. Adaptive Noise Reduction addresses this problem by using adaptive filters that can automatically adjust their parameters in real time, continuously optimizing the system's response to changing environments.\\ \\
+In the beginnings of the 20th century, filter techniques were limited to the use of static filters like low- or highpass filters. The fundamental techniques allow to limit the frequency sprectrum, by cutting out certain frequency like high-pitched noises. In the 1930's, the first real concept of active noise cancellation was proposed  by the German Physician Paul Lueg. Lueg patented the idea of two speakers emitting antiphase signals which cancel each other out. Though his patent was granted in 1936, back at the time, there was no technical possibility detect and process audio signals in a way, to make his noise cancellation actually work in a technical environment.\\ \\
+20 years after Luegs patent, Lawrence Fogel patented a practical concept of noise cancellation, intended for noise supression in aviation - this time, the technical cirumstances of the 1950's enabled the development of an aviation headset, lowering the overall noise experienced by pilots in the cockpit of a helicopter or an airplane \ref{fig:fig_patent}.  
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.8\linewidth]{Bilder/fig_patent.jpg}
+    \caption{Patent of a device for lowering ambient noise to improve intelligence by Lawrence Fogel in 1960 \cite{soure_patent}}
+    \label{fig:fig_patent}
+\end{figure}
+Until this point in time, the realised concepts were analog noise surpression, were a microphone measures the noise and a fixed circuit generates the antiphase signal - this means, the system only works in a specified environment and there is no real adatpiveness.\\ \\
+The final step to real adaptive noise cancellation was made with the introduction of the fundamental Least-Mean-Square (LMS) algorithm in 1960 by Widrow & Hoff.
 \subsection{Introduction to the Least Mean Square algorithm for adaptive filtering}
+, allowing an automatic adaption of the filter coefficients depending on the surrounding by stepwise minimization of the squared error \\ \\
 \subsection{Signal flow diagram showing the origin of the useful signal,
 noise signal, and their coupling}
 \subsection{Derivation of the system’s transfer function based on the problem setup}