Plots überarbeitet

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chapter_05.aux

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chapter_05.tex

@@ -1,5 +1,5 @@
 \section{Performance evaluation of different implementation variants}
-To verify the general performance of the \ac{DSP} implemented \ac{ANR} algorithm, the complex usecase of the high-level implemenation is used, which includes, again, a 16-tap \ac{FIR} filter and an update of the filter coefficients every cycle. In contary to the high-level implementation, the coeffcient convergence is now not included in the evaluation anymore, but the metric for the \ac{ANR} performance stays the same as the \ac{SNR} improvement.
+To verify the general performance of the \ac{DSP}-implemented \ac{ANR} algorithm, the complex usecase of the high-level implemenation is utilized, which includes, again, a 16-tap \ac{FIR} filter and an update of the filter coefficients every cycle. In contary to the high-level implementation, the coeffcient convergence is now not included in the evaluation anymore, but the metric for the \ac{ANR} performance stays the same as the \ac{SNR} improvement.
 \begin{figure}[H]
     \centering
     \includegraphics[width=1.0\linewidth]{Bilder/fig_plot_1_dsp_complex.png}
@@ -9,23 +9,32 @@ To verify the general performance of the \ac{DSP} implemented \ac{ANR} algorithm
 \begin{figure}[H]
     \centering
     \includegraphics[width=1.0\linewidth]{Bilder/fig_plot_2_dsp_complex.png}
-    \caption{Error signal and filter coefficient evolution of the complex \ac{ANR} use case, simulated on the \ac{DSP}}
+    \caption{Error signal of the complex \ac{ANR} use case, simulated on the \ac{DSP}}
     \label{fig:fig_plot_2_dsp_complex.png}
 \end{figure}  
-Figure \ref{fig:fig_plot_1_dsp_complex.png} and \ref{fig:fig_plot_2_dsp_complex.png} show the results of the complex \ac{ANR} use case, simulated on the \ac{DSP}. The \ac{SNR} improvement of XXXX dB is nearly the same as the one of the high-level implementation, which is XXXX dB. The small difference can be explained by the fact that the \ac{DSP} implementation is based on fixed-point arithmetic, which leads to a slightly different convergence behavior. Nevertheless, the results show that the \ac{DSP} implementation of the \ac{ANR} algorithm is able to achieve a similar performance as the high-level implementation, again indicating the fact, that 16 filter coefficients are insufficent to filter out a complex, phase-shifted noise signal. The next step is of evaluate the performance of the \ac{DSP} implementation in terms of computational efficiency under different scenarios.
+\noindent Figure \ref{fig:fig_plot_1_dsp_complex.png} and \ref{fig:fig_plot_2_dsp_complex.png} show the results of the complex \ac{ANR} use case, simulated on the \ac{DSP}. The \ac{SNR} improvement of 5.92 dB is similar to the one of the high-level implementation, which is 5.14 dB. The small difference can be explained by the fact that the \ac{DSP} implementation is based on fixed-point arithmetic, which leads to a slightly different convergence behavior. Nevertheless, the results show that the \ac{DSP} implementation of the \ac{ANR} algorithm is able to achieve a similar performance as the high-level implementation, again indicating the fact, that 16 filter coefficients are insufficent to filter out a complex, phase-shifted noise signal. The next step is of evaluate the performance of the \ac{DSP} implementation in terms of computational efficiency under different scenarios.
 \subsection{Computational efficiency evaluation}
-\noindent For the evaluation of the computational efficiency, different combinations of desired signals and noise signals are considered. This approach rules out, that a certain combination of signals is not representative for the overall performance of the \ac{ANR} algorithm.
-The desired signals are chosen as follows:
+\noindent For the evaluation of the computational efficiency, different signal combinations, which are to be expected everyday situiations for a \ac{CI} patient, are considered. This approach rules out, that a certain combination of signals is not representative for the overall performance of the \ac{ANR} algorithm.
+The desired signal of a male voice over speaker is now corrupted with 5 different noise signals:
 \begin{itemize}
-    \item A male speaker on TV
-    \item A short music jingle
+    \item Breathing noise: Already used in the high-level implementation, this noise signal is a typical noise source for \ac{CI} patients, especially in quiet environments. It consists out of slowly rising and falling peaks.
+    \item Coughing noise: This noise signal is generated by coughing and consists out few, but long lasting peaks.
+    \item Scratching noise: This noise signal is generated by scratching some material, like the hair or clothes. It consists out of a high number of sharp peaks.
+    \item Drinking Noise: This noise signal is generated by drinking and consists out of a low number of peaks, which are not as sharp as the ones of the scratching noise, but still more sharp than the ones of the breathing and coughing noise.
+    \item Chewing Noise: This noise signal is generated by chewing and consists out of a high number of peaks of different amplitude.
 \end{itemize}
-PLOT
-These two desired signals are corrupted with 3 different noise signals:
-\begin{itemize}
-    \item The already used breathing sound
-    \item A chewing sound
-    \item A scratching sound
-\end{itemize}
-PLOT
-The combination of stated sets delivers 6 different scenarious, everyone different in regard of it's challenges for the \ac{ANR} algorithm. For every scenario, the \ac{SNR}-Gain is calculated with an increasing set of filter coeffcients, ranging from 16 to 64.
+The vizualization of the noise signals is shown in figure \ref{fig:fig_noise_signals.png}.
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=1.0\linewidth]{Bilder/fig_noise_signals.png}
+    \caption{Noise signals used to corrupt the desired signal in the computational efficiency evaluation}
+    \label{fig:fig_noise_signals.png}
+\end{figure}  
+The combination of stated sets delivers five different scenarious, everyone different in regard of it's challenges for the \ac{ANR} algorithm. For every scenario, the \ac{SNR}-Gain is calculated with an increasing set of filter coeffcients, ranging from 16 to 64.
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=1.0\linewidth]{Bilder/fig_snr_comparison.png}
+    \caption{Python simulation of the to be expected \ac{SNR}-Gain for different noise signals and filter lengths applied to the desired signal of a male speaker. The applied delay between the signals amounts 2ms. The graphes are smoothed by a third order savigol filter.}
+    \label{fig:fig_snr_comparison.png}
+\end{figure}  
+Figure \ref{fig:fig_snr_comparison.png} shows the expected \ac{SNR}-Gain for the different noise signals and filter lengths. The results shows, that a minimum filter length of about 32 taps is required, before (in any case) a rise in the \ac{SNR}-Gain can be observed. 

main.bbl-SAVE-ERROR

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+  {\@latex@error
+     {Missing 'biblatex' package}
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+      \aftergroup\endinput}
+  {}
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+
+
+\refsection{0}
+  \datalist[entry]{none/global//global/global}
+    \entry{source_synchrony}{misc}{}
+      \name{author}{1}{}{%
+        {{hash=ffb45f0281705211553241b831eeaf22}{%
+           family={MED-EL},
+           familyi={M\bibinithyphendelim E\bibinitperiod}}}%
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+      \field{labeltitlesource}{title}
+      \field{howpublished}{\url{https://www.medel.com/de/hearing-solutions/cochlear-implants}}
+      \field{note}{Accessed on 20.01.2026}
+      \field{title}{MED-EL Synchrony Cochlear Implant with a Sonnet 3 Audio Processor}
+      \field{year}{2026}
+    \endentry
+    \entry{source_electrode}{misc}{}
+      \name{author}{1}{}{%
+        {{hash=ffb45f0281705211553241b831eeaf22}{%
+           family={MED-EL},
+           familyi={M\bibinithyphendelim E\bibinitperiod}}}%
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+      \field{labeltitlesource}{title}
+      \field{howpublished}{\url{https://blog.medel.pro/de/chirurgie/relevanz-der-ci-elektrode/}}
+      \field{note}{Accessed on 30.01.2026}
+      \field{title}{Visualization of a MED-EL electrode inserted into a human cochlear}
+      \field{year}{2026}
+    \endentry
+    \entry{source_dsp_ch1}{misc}{}
+      \name{author}{1}{}{%
+        {{hash=26ad37f8f6f878dc28acadb561885bff}{%
+           family={Li\bibnamedelima Tan},
+           familyi={L\bibinitperiod\bibinitdelim T\bibinitperiod},
+           given={Jean\bibnamedelima Jiang},
+           giveni={J\bibinitperiod\bibinitdelim J\bibinitperiod}}}%
+      }
+      \strng{namehash}{26ad37f8f6f878dc28acadb561885bff}
+      \strng{fullhash}{26ad37f8f6f878dc28acadb561885bff}
+      \strng{bibnamehash}{26ad37f8f6f878dc28acadb561885bff}
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+      \field{sortinithash}{9381316451d1b9788675a07e972a12a7}
+      \field{labelnamesource}{author}
+      \field{labeltitlesource}{title}
+      \field{howpublished}{Elsevier Inc.}
+      \field{note}{Chapter 1}
+      \field{title}{Digital Signal Processing Fundamentals and Applications 3rd Ed}
+      \field{year}{2013}
+    \endentry
+    \entry{source_dsp_ch2}{misc}{}
+      \name{author}{1}{}{%
+        {{hash=26ad37f8f6f878dc28acadb561885bff}{%
+           family={Li\bibnamedelima Tan},
+           familyi={L\bibinitperiod\bibinitdelim T\bibinitperiod},
+           given={Jean\bibnamedelima Jiang},
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+      }
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+      \strng{fullhash}{26ad37f8f6f878dc28acadb561885bff}
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+      \strng{authorbibnamehash}{26ad37f8f6f878dc28acadb561885bff}
+      \strng{authornamehash}{26ad37f8f6f878dc28acadb561885bff}
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+      \field{labeltitlesource}{title}
+      \field{howpublished}{Elsevier Inc.}
+      \field{note}{Chapter 2}
+      \field{title}{Digital Signal Processing Fundamentals and Applications 3rd Ed}
+      \field{year}{2013}
+    \endentry
+    \entry{source_patent}{misc}{}
+      \name{author}{1}{}{%
+        {{hash=93a5470d20a141eea1a1f94678d4a447}{%
+           family={Fogel},
+           familyi={F\bibinitperiod},
+           given={Lawrence\bibnamedelima J.},
+           giveni={L\bibinitperiod\bibinitdelim J\bibinitperiod}}}%
+      }
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+      \strng{fullhash}{93a5470d20a141eea1a1f94678d4a447}
+      \strng{bibnamehash}{93a5470d20a141eea1a1f94678d4a447}
+      \strng{authorbibnamehash}{93a5470d20a141eea1a1f94678d4a447}
+      \strng{authornamehash}{93a5470d20a141eea1a1f94678d4a447}
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+      \field{labelnamesource}{author}
+      \field{labeltitlesource}{title}
+      \field{howpublished}{United States Patent Office}
+      \field{note}{Pat. Nr. 2966549}
+      \field{title}{Apparatus for improving intelligence under high ambient noise levels}
+      \field{year}{1960}
+    \endentry
+    \entry{source_dsp_ch9}{misc}{}
+      \name{author}{1}{}{%
+        {{hash=26ad37f8f6f878dc28acadb561885bff}{%
+           family={Li\bibnamedelima Tan},
+           familyi={L\bibinitperiod\bibinitdelim T\bibinitperiod},
+           given={Jean\bibnamedelima Jiang},
+           giveni={J\bibinitperiod\bibinitdelim J\bibinitperiod}}}%
+      }
+      \strng{namehash}{26ad37f8f6f878dc28acadb561885bff}
+      \strng{fullhash}{26ad37f8f6f878dc28acadb561885bff}
+      \strng{bibnamehash}{26ad37f8f6f878dc28acadb561885bff}
+      \strng{authorbibnamehash}{26ad37f8f6f878dc28acadb561885bff}
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+      \field{howpublished}{Elsevier Inc.}
+      \field{note}{Chapter 9}
+      \field{title}{Digital Signal Processing Fundamentals and Applications 3rd Ed}
+      \field{year}{2013}
+    \endentry
+  \enddatalist
+\endrefsection
+\endinput
+