\section{Theoretical Background}
The following subsections shall provide the reader with the theoretical background of digital signal processing to explain the implementation of ANR on a DSP, 
We will beginn with the fundamentals of digital signal processing in general, covering transfer-funtions and filters.
To fully understand ANR, a short deep-dive into the LMS algrotihm is indispensable.
From there we will continue into the histroy and the mathematical concepts of ANR, which will bring us the core of ANR, the LMS Algorithm
With this knowledge covered, we will construct the real-world signal flow diagrams and transfer functions, of an implanted CI system essential to implement a functioning ANR on a low-power DSP.
At the end of chapter two, several Python simualtions shall function as a practical demonstrations of the recently presented thereotec background
To accomplish that, we will be relying on the book Digital Signal Processing Fundamentals and Applications 2nd Ed 
\subsection{Fundamentals of digital signal processing, transfer functions and filters}
\subsection{Explanation of Finite Impulse Response- and Infinite Impulse Response-filters}
\subsection{Introduction to Adaptive Nose Reduction}

\subsection{Introduction to the Least Mean Square algorithm for adaptive filtering}
\subsection{Signal flow diagram showing the origin of the useful signal,
noise signal, and their coupling}
\subsection{Derivation of the system’s transfer function based on the problem setup}
\subsection{Example applications and high-level simulations using Python}