18 lines
1.5 KiB
TeX
18 lines
1.5 KiB
TeX
\section{Theoretical Background}
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The following subsections shall provide the reader with the theoretical background of digital signal processing to explain the implementation of ANR on a DSP,
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We will beginn with the fundamentals of digital signal processing in general, covering transfer-funtions and filters.
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To fully understand ANR, a short deep-dive into the LMS algrotihm is indispensable.
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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
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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.
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At the end of chapter two, several Python simualtions shall function as a practical demonstrations of the recently presented thereotec background
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To accomplish that, we will be relying on the book Digital Signal Processing Fundamentals and Applications 2nd Ed
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\subsection{Fundamentals of digital signal processing, transfer functions and filters}
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\subsection{Explanation of Finite Impulse Response- and Infinite Impulse Response-filters}
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\subsection{Introduction to Adaptive Nose Reduction}
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\subsection{Introduction to the Least Mean Square algorithm for adaptive filtering}
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\subsection{Signal flow diagram showing the origin of the useful signal,
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noise signal, and their coupling}
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\subsection{Derivation of the system’s transfer function based on the problem setup}
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\subsection{Example applications and high-level simulations using Python}
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