Korr 5

chapter_03.tex

@@ -1,7 +1,7 @@
 \section{High level simulations}
 The main purpose of the high-level simulations is to verify and demonstrate the theoretical approach of the previous chapters and to evaluate the performance of the proposed algorithms under various conditions.  The following simulations include different scenarios such as, different types of noise signals and different considerations of transfer functions. The goal is to verify different approaches before taking the step to the implementation of said algorithms on the low-power \ac{DSP}.\\ \\
 The implementation is conducted in Python, which provides a flexible environment for numerical computations and data visualization. The simulation is graphically represented using the Matplotlib-library. 
-\subsection{Adaptive Noise Reduction algorithm implementation}
+\subsection{Adaptive noise reduction algorithm implementation}
 The high-level implementation of the \ac{ANR} algorithm follows the theoretical framework outlined in Subchapter 2.5, specifically Equation \ref{equation_lms}. The algorithm is designed to adaptively filter out noise from a desired signal using a reference noise signal. The implementation of the \ac{ANR} function includes the following key steps:
 \begin{itemize}
     \item Initialization: Define arrays to store the reference noise samples (Sample Line), the filter coefficients (Filter Line), the processed output samples (output), and the updated filter coefficients (coefficient\_matrix) over time. Then a sequence of input samples is processed iteratively.