Abage 2

chapter_03.tex

@@ -98,7 +98,7 @@ The last on of our three simplified use cases involves the use of a Gaussian whi
     \caption{Error signal and filter coefficient evolution of simple use case 3}
     \label{fig:fig_plot_2_noise.png}
 \end{figure}
-\subsection{Intermediate use case}
+\subsection{Intermediate ANR use case}
 After the general functionality of the \ac{ANR} algorithm has been verified with the above simple and artificial use cases, a more complex and intermediate scenario is now introduced. In this use case, a real-world audio track of a person speaking on TV (see top graph in Figure \ref{fig:fig_plot_1_wav.png}) is used as the desired signal, which is then corrupted with a dominant breathing noise as the noise signal. This scenario represents a more realistic application of the \ac{ANR} algorithm, as it involves complex audio signals with varying frequency components and relatively high dynamics, but still keeps the advantage of having the clean signal available for performance evaluation. Also, again, the same noise which corrupts the desired signal is used as the reference noise signal, as no transfer functions are applied on the signals.
 \begin{figure}[H]
     \centering