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patha
2026-03-01 12:26:00 +01:00
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chapter_05.tex
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@@ -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 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 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.
\begin{figure}[H]
\centering
\includegraphics[width=1.0\linewidth]{Bilder/fig_plot_1_dsp_complex.png}
@@ -12,4 +12,20 @@ To verify the general performance of the \ac{DSP} implemented \ac{ANR} algorithm
\caption{Error signal and filter coefficient evolution 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 5.92 dB is nearly the same as the one of the high-level implementation, which is 6.15 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. The next step is of evaluate the performance of the \ac{DSP} implementation in terms of computational efficiency under different scenarios.
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.
\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:
\begin{itemize}
\item A male speaker on TV
\item A short music jingle
\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.