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Patrick Hangl
2026-01-28 15:38:02 +01:00
parent a9461c05bf
commit f339e4bad1
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chapter_03.tex
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@@ -41,8 +41,8 @@ The flow diagram in Figure \ref{fig:fig_anr_logic} illustrates the logical flow
return output, coefficient_matrix
\end{lstlisting}
\label{fig:fig_anr_code}
\caption{High-level implementation of the \ac{ANR} algorithm in Python}
\label{fig:fig_anr_code}
\end{figure}
\noindent The algorithm implementation shall now be put under test by different use cases to demonstrate the functionality and performance under different scenarios, varying from simple to complex ones. Every use case includes graphical representations of the desired signal, the corrupted signal, the reference noise signal, the filter output, the error signal and the evolution of selected filter coefficients over time. In contrary to a realistic setup, the desired signal is available, allowing to evaluate the performance of the algorithm in a clear way. The performance of the \ac{ANR} algorithm is evaluated based on the error between the desired signal and the filter output, complemented with the normalized integrated squared error.
\subsection{Simple ANR use cases}