\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 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}
    \caption{Desired signal, corrupted signal, reference noise signal and filter output of the complex \ac{ANR} use case, simulated on the \ac{DSP}}
    \label{fig:fig_plot_1_dsp_complex.png}
\end{figure}
\begin{figure}[H]
    \centering
    \includegraphics[width=1.0\linewidth]{Bilder/fig_plot_2_dsp_complex.png}
    \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}  
\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 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 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}
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.