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   Exposé of Master´s Thesis
   
   ~
   
   {\LARGE\bfseries
      Implementation of Adaptive Noise Reduction in Cochlear Implant Systems 

   \large
   Name: Patrick Hangl
   
   Matriculation Number: q4179749
   
   %E-Mail: patha99@yahoo.de
   
   Course of study: M.Sc. Praktische Informatik
   
   Timeframe: September 2025 - September 2026
   
   Supervisor: Prof. Dr. Zhong Li, Dr.-Ing. Christoph Hornsteiner
}
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\section{Problem Statement}

\subsection{Motivation}

A Cochlear Implant (CI) System is a specialized form of hearing aid, used to restore partly or complete deafness. In contrary to standard hearing aids, CI's do not just amplify the audio signal received by the ear, but stimulate the auditory nerve itself directly through electric pulses.\\ \\
Usually, a CI System consists out of an external processor (''audio processor'') receiving the ambient audio signal, processing it, and then transmitting it inductively via a transmission coil through the skin to the cochlear implant itself, implanted on the patient's skull (see figure \ref{fig:fig_snychrony}). The CI stimulates the auditory nerves inside the cochlear through charge pulses, thus enabling the patient to hear the received audio signal as sound.\\ 
\begin{figure}
    \centering
    \includegraphics[width=0.5\linewidth]{Bilder/fig_synchrony.png}
    \caption{Sketch of a MED-EL Synchrony Cochlear Implant with a Sonnet 3 Audio Processor \cite{source_synchrony}}
    \label{fig:fig_snychrony}
\end{figure}
\\As for any head worn hearing aid, the audio processor of a CI system does not only pick up the desired ambient audio signal, but also any sort of interference noises from different sources. This circumstance leads to a decrease in the quality of the final audio signal. Reducing this interference noise through Adaptive Noise Reduction (ANR), implemented on a low-power Digital Signal Processor (DSP), which can be powered within the electrical limitations of a CI system, is the topic of this master's thesis.
\subsection{Task Definition}
\begin{itemize}
    \item The thesis introduces hearing aids and cochlear implants, outlines the general problem of noise interference and defines the goal of ANR implementation on dedicated low-power hardware.
    \item It presents the theoretical foundations including signal processing, adaptive filtering, and system modeling supported by high-level Python simulations.
    \item The thesis covers hardware setup, efficient firmware implementation and evaluation of different approaches through low-level simulations. 
    \item Finally, the most promising approach is implemented on a low-power DSP, showcasing the performance with real-world audio examples.
\end{itemize}

\subsection{Expected Results}
\begin{itemize}
    \item The first objective of this thesis is to provide a general overview on adaptive filtering algorithms, which is further illustrated through high-level simulations conducted in Python. 
    \item The second objective of this thesis is the presentation of a functional code implementation within the dedicated development environment of the target hardware. Various approaches to the ANR algorithm are low-level simulated on the dedicated hardware and compared to evaluate their performance.
    \item The third objective of this thesis is the successful implementation of the most promising algorithm approach on a low-power DSP, which is part of an integrated System on Chip (SoC), being evaluated by using real-world audio examples.
\end{itemize}

\section{Company background and current standard}
MED-EL is a global leader in innovative hearing solutions for people with hearing loss. The company specializes in the research, development, manufacturing, and distribution of implantable and non-implantable hearing solutions and employs over 2,500 people worldwide. The company’s headquarters, including research, development, and production, is located in Innsbruck, Austria.\\ \\
MED-EL offers different hearing solutions, including the following systems:
\begin{itemize}
    \item Cochlear Implants designed for individuals with severe to profound sensorineural hearing loss.
    \item Middle Ear Implants for conductive or mixed hearing loss
    \item Bone Conduction Implants for conductive or mixed hearing loss and single-sided deafness
\end{itemize}
Every of the above examples in general work the same - the ambient audio signal is recorded by a microphone inside the external audio processor, processed and then send via the transmission coil to the CI implanted on the patient's skull. Beside the desired ambient signal, interference noises of all sorts mix with the primary signal and lead to a disturbance in the overall quality of the audio signal received by the patient.\\ \\
As the CI system is powered by a small battery located in the audio processor, energy efficiency is crucial for a possible solution of the described problem of noise interference. Any approach to a reduction of interference noise must be highly optimized with regard to computing power and implemented on dedicated low-power hardware, being able to be powered within the limitations of a CI system.
\section{Solution Concept}
This thesis will focus on the two following approaches, aiming for a satisfying solution through the combination of both:
\begin{itemize}
    \item Algorithm optimization: The main solution concept of this thesis is the optimization of the adaptive filter of the ANR algorithm in combination with the used low-power hardware. Its goal is, to deliver the best possible result in interference noise reduction while still being able to be powered by the limited resources of a CI system.\\
    Different variants, like the fully adaptive filter, the hybrid static/adaptive filter and different optimization approaches of the latter one are low-level simulated on the dedicated DSP. Especially, the different optimization strategies of the hybrid static/adaptive filter algorithm shall be evaluated and compared in regard of their required computing power, and therefore, their required power consumption.\\ Depending on the kind of interference noise, the frequency and the intensity, a promising optimization approach is the reduction of adaptation steps per sample while still maintaining an adequate quality of the filtered audio signal.
    \begin{figure}
    \centering
    \includegraphics[width=0.8\linewidth]{Bilder/fig_anr.png}
    \caption{Schematic of an ANR system \cite{source_anr}}
    \label{fig:fig_anr}
    \end{figure}
    \item Efficient firmware implementation: Due to the fact, that the CI system is powered by a battery with a relatively small capacity, the firmware is required to work with the least power possible. Therefore, optimization in regard to a minimization of needed processor clocks is aimed for.\\ 
\end{itemize}

\section{Preliminary structure}

\begin{itemize}
\item Chapter 1: Introduction
    \begin{itemize}
        \item Overview of hearing aids and their role in auditory assistance
        \item Introduction to Cochlear Implant (CI) Systems and Audio Processors
        \item Problem description: Interference signals mixed with the ambient audio signals in Audio Processors
        \item Formulation of the objective of the thesis: Implementation of Adaptive Noise Reduction (ANR) on a dedicated low-power Digital Signal Processor (DSP)
    \end{itemize}
\item Chapter 2: Theoretical Background
    \begin{itemize}
        \item Fundamentals of signal theory and transfer functions, including a simple illustrative example
        \item Introduction to ANR
        \item Explanation of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters
        \item Introduction to the Least Mean Square (LMS) method for adaptive filtering
        \item Problem analysis: Signal flow diagram showing the origin of the useful signal, noise signal, and their coupling
        \item Derivation of the system’s transfer function based on the problem setup
        \item Example applications and high-level simulations using Python
        \end{itemize}
\item Chapter 3: Hardware and low-level simulation of different algorithm approaches
    \begin{itemize}
        \item Hardware description
        \begin{itemize}
            \item Necessity of usage of low-power components in CI Systems
            \item Architecture of the DSP and ARM Core System on Chip (SoC)
            \item Justification for using a DSP instead of the ARM Core SoC
        \end{itemize}
        \item System setup
        \begin{itemize}
            \item Overview of the test board, debugging tools, and development environment
            \item Efficient implementation of the FIR filter on the DSP
        \end{itemize}
        \item Low-level simulations of different algorithm approaches
        \begin{itemize}
            \item Fully adaptive filter 
            \item Hybrid static/adaptive filter
            \item Optimization of the hybrid static/adaptive filter
            \begin{itemize}
                \item Variation of filter length (number of coefficients): accuracy vs. computational cost
                \item Adaptation timing strategies: fixed interval vs. dynamic adaptation based on error metrics
            \end{itemize}
        \end{itemize}
    \end{itemize}
\item Chapter 5: Comparison of different approaches and implementation on hardware
    \begin{itemize}    
    \item Comparison of optimized vs. non-optimized approaches 
    \item Computing power and approximate power consumption analysis of the DSP
    \item Implementation of the most promising approach on a low-power DSP
    \item Real-world testing using an evaluation board and audio measurement systems
    \end{itemize}
\item Chapter 6: Conclusion


\end{itemize}


\section{Preliminary timeframe}

\begin{itemize}
\item September 2025: Begin of the Thesis
\item October 2025: Hardware Setup and Introduction
\item November 2026: Theory and high-level simulations
\item February 2026: Low-level simulations on hardware and comparison
\item May 2026: Implementation on a DSP
\item June 2026: Conclusion
\item July 2026: 1st version of the thesis
\item August 2026: 2nd version of the thesis
\item September 2026: Presentation of the thesis
\end{itemize}

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