6

chapter_06.tex

@@ -1 +1,9 @@
-\section{Conclusion and outlook}
+\section{Conclusion and outlook}
+The focus of this thesis was to investigate the possibilities for the efficient implementation of a real-time capable \ac{ANR} algorithm in \ac{CI} systems. The initial high-level implementation in Python proofed the general feasibility of the proposed method, where the \ac{SNR}-Gain was introduced as a metric for the quality of the \ac{ANR} algorithm. Said metric was used to evaluate the performance of the algorithm in various settings andnoise conditions. First a fictional signal and noise signal were used to check the algrotihm for it´s general functionality. Then the step to a real recorded signal was made. The final and most complex combination,s (which then served as a benchmark for the remaining implementations) was the use of a real wordls signals, which passed different transfer functions and delays. In every case, the algorithm was able to achieve significant performance improvement in the \ac{SNR} for the processed signals. \\ \\
+\noindent The next challenge was to implement the algorithm in a efficient way in the C prgramming language, to achieve real-time capability. This was achieved by the use of \ac{DSP} cimpiler instrinsic functions, which allow to perform logic operations wih a minimum of needed instructions. After the C-implementation was fucntional, the performacne in the case of the benchmark track was compared to the initial python implementation. A histrogram of the differences between the two ouptuts shows only minor deviations, which can be attributed to the fixed-point calculations of the \ac{DSP} compiler.\\ \\
+\noindent With the working C-implementation in place, a closer look on the performance, especially the needed cycles to compute on sample, was taken - the resulting formula is a function of the filter length and the update rate. With this information in mind, several noise sources were put under test, to evaluatue the optimal filter length, which is a trade-off between the performance improvement and the computational cost - the result was 45 coefficients.\\ \\
+With a known filter length of 45 coefficients, the final improvement of the algorithm regarding performance and computational cost could be evaluated. The base was the computational most costly full-update implementation, needing 357 cycles to process one sample.\\ \\ 
+\noindent The first approach was a rather simple reduction in the update rate, evaluated for the benchmark case and different signal/noise combinations. The result was a significant reduction in the needed cycles, but with a also quite significant drop in the \ac{SNR}-Gain. Additionaly, the implementation of such an universal reduction, required computational expensive processor operations, further reducing the cost-benefit-ration.\\ \\ 
+\noindent The second approach was the proposed method of an error driven optimization, utilizing the idea of a fixed threshold for the error signal. Again, evaluated for the benchmark case and different signal/noise combinations, this approach can be considered a success, as it was able to achieve a significant reduction in the needed cycles, while only reducing the \ac{SNR}-Gain by a small amount. The implementation of this method is also computationally efficient, as it only requires a simple comparison operation to check if an update is necessary.\\ \\
+\noindent The final result of this thesis shows, that the second method of an error driven optimization, utilizing the idea of a fixed threshold for the error signal, is a viable method to achieve significant performance improvement, reducing the computational load of the \ac{DSP} Core by over 62\% while only redcuing the \ac{SNR}-Gain by roughly 12\%.\\ \\
+\noindent For future work, a proposed method to further optimize the system would be the use of a dynamic threshold, which could be adapted to the current noise conditions. The background for this proposal is the fact, that beside the error-signal, also the noise signal itself influences the size of the filter-coeffcient update. In the current implementation, the threshold is only dependend on the error signal - if a sitatuion arises, where the noise signal is very small, but the error/output signal is high due to a a input signal, an update of the filter coefficients would be triggered, even if not necessary. A dynamic threshold, which also takes the noise signal into account, could further reduce the number of updates, but with a potentially higher computational effort.