2.1, 2.2

chapter_01.tex

@@ -1,19 +1,19 @@
 \section{Introduction}
 \subsection{Motivation}
-According to the World Health Organisation (WHO), around 1.6 billion people over 14 years worldwide suffer from any kind of hearing loss. Included in this 1.6 billion people, around 430 million suffer from disabling hearing loss (up to deafness), requiring rehabilitation. In the case of disabling hearing loss, the possiblity of using a Implant System has revolutionized auditory rehabilitation by restoring partial hearing. Despite a steady progress in implant technology over the past decades, the system still faces its limitations. Complex auditory environments, like static noises overlain by a person speaking, can still propose a considerable challenge for users of auditory implants comapred to people with a healty hearing. \\ \\
-Therefore, the improvement of implant performance in regard to the suppresion of distrubance noises is therefore a crucial step in the development of more user-friendly implant solutions which provide users with more natural sound perception and greater listening comfort.
+According to the World Health Organisation (WHO), around 1.6 billion people over 14 years worldwide suffer from any kind of hearing loss. Included in this 1.6 billion people, around 430 million suffer from disabling hearing loss (up to deafness), requiring rehabilitation. In the case of disabling hearing loss, the possiblity of using a Implant System has revolutionized auditory rehabilitation by restoring partial hearing. Despite a steady progress in implant technology over the past decades, the system still faces its limitations. Complex auditory environments, like static noises overlain by a person speaking, can still propose a considerable challenge for users of auditory implants comapred to people with a healthy hearing. \\ \\
+Therefore, the improvement of implant performance in regard to the suppression of disturbance noises is therefore a crucial step in the development of more user-friendly implant solutions which provide users with more natural sound perception and greater listening comfort.
 \\ \\
 By addressing these challenges, this work aims to contribute to the next generation of cochlear implant technology, ultimately enhancing the auditory experience and quality of life for people with severe hearing impairments.
 \subsection{Introduction to Cochlear Implant Systems}
 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_synchrony}). The CI stimulates the auditory nerves inside the cochlear through charge pulses, thus enabling the patient to hear the received audio signal as sound.\\ 
+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_synchrony}). 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}[H]
     \centering
     \includegraphics[width=0.6\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_synchrony}
 \end{figure}
-The pulse transmission to the cochlear is realized through a silicone eletrode with embedded metal contacts. Said electrode is inserted into the cochlear through a drilled hole in the bone, where, depending on the insertion-depth, different contact-areas stimulate different parts of the frequency-spectrum of the hearing sense. The smaller end of the electrode-array inserted deep into the cochelar stimulate low-frequencies, whereas the larger beginning of the array stimulates high-frequencies. (see figure \ref{fig:fig_electrode}).
+The pulse transmission to the cochlear is realized through a silicone electrode with embedded metal contacts. Said electrode is inserted into the cochlear through a drilled hole in the bone, where, depending on the insertion-depth, different contact-areas stimulate different parts of the frequency-spectrum of the hearing sense. The smaller end of the electrode-array inserted deep into the cochlear stimulate low-frequencies, whereas the larger beginning of the array stimulates high-frequencies. (see figure \ref{fig:fig_electrode}).
 \begin{figure}[H]
     \centering
     \includegraphics[width=0.8\linewidth]{Bilder/fig_electrode.jpg}
@@ -22,12 +22,21 @@ The pulse transmission to the cochlear is realized through a silicone eletrode w
 \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{The problem of signal interference in audio processing}
-A signal is a physical parameter (e.g. pressure, voltage) changing its value over time. The termn "Signal Interference" describes the overlapping of two or more signals resulting in a new signal. \\ \\A simple example of a desirable signal interference would be the sound generated by playing several strings of a guitar. Hitting one string results in a pure sine wave of a designated frequency (depending on which note is played), perceptible as sound. Hitting a chord (consisting of several strings), the seperate sine waves of the strings combine to a new signal through the process of signal interference - in this case a desired, harmonic sound. (see XXX)
-In technical applications on the other hand, signal interference can cause a considerable degradation to the quality of the final signal, posing an additional challenge to aurally impaired people using a implant solution for rehabilitation. Fig XXX shows exemplary the comparison between the undisturbed audio signal of a person speaking and the audio signal of a person speaking disturbed by a strong static noise. Thus, the objetive of this thesis shall be the improvement of implant technology in regard of noise reduction.
+A signal is a physical parameter (e.g. pressure, voltage) changing its value over time. The term "Signal Interference" describes the overlapping of two or more signals resulting in a new signal. \\ \\A simple example of a desirable signal interference would be the sound generated by playing several strings of a guitar. Hitting one string results in a pure sine wave of a designated frequency (depending on which note is played), perceptible as sound. Hitting a chord (consisting of several strings), the separate sine waves of the strings combine to a new signal through the process of signal interference - in this case a desired, harmonic sound. (see Figure \ref{fig:fig_interference})
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.8\linewidth]{Bilder/fig_interference.png}
+    \caption{Signal interference of three separate tones resulting in an E-Minor chord.}
+    \label{fig:fig_interference}
+\end{figure}
+
+In technical environments signal interference is also common when electromagnetic and acoustic noise coexist. Such conditions can cause electromagnetic coupling or broadband acoustic noise that degrades microphone input and digital transmission
+Therefor, in auditory applications, signal interference can cause a considerable degradation to the quality of the final signal, posing an additional challenge to aurally impaired people using an implant solution for rehabilitation. 
+Thus, the objective of this thesis shall be the improvement of implant technology in regard of noise reduction.
 \subsection{Implementation of Adaptive Noise Reduction in Cochlear Implant Systems}
-The above problem statement of signal interference shows its significance in the improvement of CI systems. For persons with a healty hearing sense, the addition of noise to a observed signal may just mean a decrease in hearing comfort, wheras for aurally impaired people it can make the difference in the basic understanding of information. As everyday environments present fluctuating background noise - from static crowd chatter to sudden sounds of different characteristics — that can severely degrade speech perception, the ability to surpress noise is a crucial benefit for users of cochlear implant systems. \\ \\
-Adaptive Noise Reduction (ANR) (also commonly reffered as Adaptive Noise Cancellation (ANC)), is an advanced signal-processing technique that adjusts the parameters of a digital filter to surpress unwanted noise from a signal while preserving the desired target-signal. In contrary to static filters (like a high- or low-pass filter), ANR uses realtime feedback to ajust said digital filter to adapt to the current cirumstances.\\ \\
-The concept of ANR was already proposed in the the 1930´s by the german Physician Paul Lueg in form of two antiphase signales canelling eacht other, but the fundamental Least-Mean-Square algorithm to realize a technical implementation of ANR itself was developed not until towards the end of the 1950s. The first commercial use of ANR was finally seen in form of an aviation headset for pilots in 1989.\\ \\
+The above problem statement of signal interference shows its significance in the improvement of CI systems. For persons with a healthy hearing sense, the addition of noise to an observed signal may just mean a decrease in hearing comfort, whereas for aurally impaired people it can make the difference in the basic understanding of information. As everyday environments present fluctuating background noise - from static crowd chatter to sudden sounds of different characteristics — that can severely degrade speech perception, the ability to suppress noise is a crucial benefit for users of cochlear implant systems. \\ \\
+Adaptive Noise Reduction (ANR) (also commonly referred as Adaptive Noise Cancellation (ANC)), is an advanced signal-processing technique that adjusts the parameters of a digital filter to suppress unwanted noise from a signal while preserving the desired target-signal. In contrary to static filters (like a high- or low-pass filter), ANR uses real-time feedback to adjust said digital filter to adapt to the current circumstances.\\ \\
+The concept of ANR was already proposed in the 1930’s by the German Physician Paul Lueg in form of two antiphase signals cancelling each other, but the fundamental Least-Mean-Square algorithm to realize a technical implementation of ANR itself was developed not until towards the end of the 1950s. The first commercial use of ANR was finally seen in form of an aviation headset for pilots in 1989.\\ \\
 The challenge in the implementation of ANR in CI systems lies in the limited capacities. 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.\\ \\
 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.\\ \\
 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.

chapter_02.tex

@@ -1,29 +1,45 @@
 \section{Theoretical Background}
 The following subchapters shall equip the reader with the theoretical foundations of digital signal processing to better understand the following implementation of ANR on a low-power signal processor.\\ \\
-We will beginn with the fundamentals of digital signal processing in general, covering the transfer-funtions and filters.\\
+We will beginn with the fundamentals of digital signal processing in general, covering the transfer-functions and filters.\\
 To fully understand ANR, a short deep-dive into the concepts of Finite Impulse Respone (FIR) and Infinite Impulse Respone (IIR) filters is indispensable.\\
-From this point we will continue into the history and the mathematical concepts of ANR, its realtime feedback possibilities and its use of the Least Mean Square (LMS) Algorithm.\\
-With this knowledge covered, we will design a realstic signal flow diagram and  the corresponding transfer functions, of an implanted CI system essential to implement a functioning ANR on a low-power DSP.\\
-At the end of chapter two, high-level Python simualtions shall function as a practical demonstrations of the recently presented thereotical background.\\ \\
+From this point we will continue into the history and the mathematical concepts of ANR, its real-time feedback possibilities and its use of the Least Mean Square (LMS) Algorithm.\\
+With this knowledge covered, we will design a realistic signal flow diagram and  the corresponding transfer functions, of an implanted CI system essential to implement a functioning ANR on a low-power DSP.\\
+At the end of chapter two, high-level Python simulations shall function as a practical demonstration of the recently presented theoretical background.\\ \\
 Chapter 2 is relying on the textbook ''Digital Signal Processing Fundamentals and Applications 2nd Ed'' by Tan and Jiang \cite{source_dsp1}.
 \subsection{Fundamentals of digital signal processing, transfer functions and filters}
 Digital Signal Processing (DSP) describes the manipulation of an analog signal trough mathematical approaches after it has been recorded and converted into a digital form. Nearly every part of the modern daily live, be it communication via cellphones, X-Ray imaging or picture editing, is affected by DSP.\\ \\
 \begin{figure}[H]
     \centering
     \includegraphics[width=0.8\linewidth]{Bilder/fig_dsp.jpg}
-    \caption{Block diagram of processing an analog input signal to an analog output signal with digital signal processing in between  \cite{source_fig_dsp}}
+    \caption{Block diagram of processing an analog input signal to an analog output signal with digital signal processing in between  \cite{source_dsp_ch1}}
     \label{fig:fig_dsp}
 \end{figure}
-Before digital signal processing can be applied to an analog signal like voice, several steps are required beforehand. An analog signal, continous in both time and amplitude, is passed through a initial filter, which limits the freqency bandwith. An analog-digital converter then samples and quantizies the signal into a digital form, now discrete in time and amplitude. This digital signal can now be processed, before (possibly) being converted to a analog signal again. (refer to figure \ref{fig:fig_dsp}).\\ \\
-A signal (either analog or digital) can be displayed and analyzed in two ways: the time spectrum and the freqency spectrum. The time spectrum shows the amplitude of the signal over time - like the sine waves from figure XXX. If a fast fourier transformation (FFT) is applied to the signal in the time spectrum, we recieve the same signal in the frequency spectrum, now showing the frequencies present in the signal (refer to figure \ref{fig:fig_fft}).\\ \\
+Before digital signal processing can be applied to an analog signal like voice, several steps are required beforehand. An analog signal, continuous in both time and amplitude, is passed through an initial filter, which limits the frequency bandwidth. An analog-digital converter then samples and quantities the signal into a digital form, now discrete in time and amplitude. This digital signal can now be processed, before (possibly) being converted to an analog signal again. (refer to figure \ref{fig:fig_dsp}).\\ \\
+A signal (either analog or digital) can be displayed and analyzed in two ways: the time spectrum and the frequency spectrum. The time spectrum shows the amplitude of the signal over time - like the sine waves from Figure \ref{fig:fig_interference}. If a fast Fourier transformation (FFT) is applied to the signal in the time spectrum, we receive the same signal in the frequency spectrum, now showing the frequencies present in the signal (refer to Figure \ref{fig:fig_fft}).\\ \\
 \begin{figure}[H]
     \centering
     \includegraphics[width=0.8\linewidth]{Bilder/fig_fft.jpg}
-    \caption{Sampled digital signal in the time spectrum and in the frequency spectrum  \cite{source_fig_fft}}
+    \caption{Sampled digital signal in the time spectrum and in the frequency spectrum  \cite{source_dsp_ch1}}
     \label{fig:fig_fft}
 \end{figure}
-
+When we discuss signals in a mathematical way, we need to explain the term ''transfer function''. A transfer function is a mathematical representation of an abstract system that describes how an input signal is transformed into an output signal. This could mean a simple amplification or a phase shift applied to an input signal.
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.8\linewidth]{Bilder/fig_transfer.jpg}
+    \caption{Simple representation of a transfer function taking a noisy input signal and delivering a clean output signal  \cite{source_dsp_ch1}}
+    \label{fig:fig_transfer}
+\end{figure}
+In digital signal processing, especially in the design of a noise reduction algorithm, transfer functions are essential for modeling and analyzing filters, amplifiers, and the auditory pathway itself. By understanding a system’s transfer function, one can predict how sound signals will be altered and therefore optimize filter parameters to deliver clearer auditory experience for the user of the implant system.\\ \\
+During the description of transfer functions, we term ''filter'' was used but not yet defined. A filter can be understood as a component in signal processing, designed to modify or extract specific parts of a signal by selectively allowing certain frequency ranges to pass while attenuating others. Filters can be static, meaning they always extract the same portion of a signal, or adaptive, meaning they change their filtering-behavior over time according to their environment. Examples for static filter include low-pass-, high-pass-, band-pass- and band-stop filters, each tailored to isolate or remove particular frequency content (refer to Figure \ref{fig:fig_lowpass}).
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.8\linewidth]{Bilder/fig_lowpass.jpg}
+    \caption{Behavior of a low-pass-filter.\cite{source_dsp_ch2}}
+    \label{fig:fig_lowpass}
+\end{figure}
+Examples for an adaptive filter is the Least-Mean-Square-Algorithm used for adaptive noise reduction, which will be introduced in the following chapters.
 \subsection{Explanation of Finite Impulse Response- and Infinite Impulse Response-filters}
+Before we continue with the introduction to the actual topic of this thesis, ANR, two very essential filter designs need further explanation.
 \subsection{Introduction to Adaptive Nose Reduction}
 
 \subsection{Introduction to the Least Mean Square algorithm for adaptive filtering}

literature.bib

@@ -28,19 +28,18 @@
   year = {2013},
   note = {ISBN: 978-0-12-415893-1}
 }
-
-@misc{source_fig_dsp,
+@misc{source_dsp_ch1,
   author = {Li Tan, Jean Jiang},
   title = {Digital Signal Processing Fundamentals and Applications 2nd Ed},
   howpublished = {Elsevier Inc.},
   year = {2013},
-  note = {Page 2-3}
+  note = {Chapter 1}
 }
-
-@misc{source_fig_fft,
+@misc{source_dsp_ch2,
   author = {Li Tan, Jean Jiang},
   title = {Digital Signal Processing Fundamentals and Applications 2nd Ed},
   howpublished = {Elsevier Inc.},
   year = {2013},
-  note = {Page 6}
+  note = {Chapter 2}
 }
+

main.tex

@@ -63,7 +63,7 @@ by \par
 \tableofcontents
 \newpage
 \begin{abstract}
-Hier steht der Text meiner Zusammenfassung.
+abstract
 \end{abstract}
 
 \include{chapter_01}