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Überschreiben der REchtschreibkorrektur

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Patrick Hangl
2026-04-25 10:26:49 +02:00
parent 66385250bc 02db7450d7
commit 52e5b87f76
3 changed files with 29 additions and 287 deletions
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chapter_04.aux
+27 -27
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chapter_05.tex
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@@ -27,10 +27,10 @@ To verify the general performance of the \ac{DSP}-implemented \ac{ANR} algorithm
\end{figure} \end{figure}
\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} - with a \ac{SNR}-Gain of 10.26 dB it performs equivalent sucessful as the one of the high-level implementation. Figure \ref{fig:fig_high_low_comparison.png} shows both outputs seperately and then together in one subfigure, together with the plotted error amplitude. Figure \ref{fig:fig_high_low_comparison_hist.png} feautres a histogram of the error amplitude between the high- and low-level implemenation, indicating the correct functionality of the \ac{DSP} implementation. The small deviations 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 the same performance as the high-level implementation. The next step is of evaluate the performance of the \ac{DSP} implementation in terms of computational efficiency under different scenarios and non-synchrone signals. \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} - with a \ac{SNR}-Gain of 10.26 dB it performs equivalent sucessful as the one of the high-level implementation. Figure \ref{fig:fig_high_low_comparison.png} shows both outputs seperately and then together in one subfigure, together with the plotted error amplitude. Figure \ref{fig:fig_high_low_comparison_hist.png} feautres a histogram of the error amplitude between the high- and low-level implemenation, indicating the correct functionality of the \ac{DSP} implementation. The small deviations 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 the same performance as the high-level implementation. The next step is of evaluate the performance of the \ac{DSP} implementation in terms of computational efficiency under different scenarios and non-synchrone signals.
\subsection{Determination of the optimal filter length} \subsection{Determination of the optimal filter length}
\noindent The main focus for evaluating the computational efficiency is the determination of the optimal filter length. To achieve this goal, different signal combinations, which are to be expected everyday situiations for a \ac{CI} patient, are considered. Again, a delay of 2 ms bewteen the corruption noise signal and the reference noise signal is applied, increasing the need for a longer filter. The desired signal of a male voice over speaker is now corrupted with 5 different noise signals, ruling out, that a certain combination of signals is not representative for the overall performance of the \ac{ANR} algorithm: \noindent The main focus for evaluating the computational efficiency is the determination of the optimal filter length. To achieve this goal, different signal combinations, which are to be expected every day situiations for a \ac{CI} patient, are considered. Again, a delay of 2 ms bewteen the corruption noise signal and the reference noise signal is applied, increasing the need for a longer filter. The desired signal of a male voice is now corrupted with 5 different noise signals, ruling out, that a certain combination of signals is not representative for the overall performance of the \ac{ANR} algorithm:
\begin{itemize} \begin{itemize}
\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 maxima. \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 maxima.
\item Coughing noise: This noise signal is generated by coughing and consists out few, but long lasting maxima, showing similarities to a rectangular function. \item Coughing noise: This noise signal is generated by coughing and consists out few, but long-lasting maxima, showing similarities to a rectangular function.
\item Scratching noise: This noise signal is generated by scratching some material with finger nails, like the hair or clothes. It consists out of a high number of sharp peaks. \item Scratching noise: This noise signal is generated by scratching some material with finger nails, like the hair or clothes. It consists out of a high number of sharp peaks.
\item Drinking Noise: This noise signal is generated by swallowing a liquid and consists out of a low number of sharp peaks, featuring long pauses between them. \item Drinking Noise: This noise signal is generated by swallowing a liquid and consists out of a low number of sharp peaks, featuring long pauses between them.
\item Chewing Noise: This noise signal is generated by consuming food and consists out of a high number of peaks of different amplitude. \item Chewing Noise: This noise signal is generated by consuming food and consists out of a high number of peaks of different amplitude.