Korr 5

chapter_05.tex

@@ -24,7 +24,7 @@ The vizualization of the five described noise signals is shown in Figure \ref{fi
 \end{figure}  
 \noindent Figure \ref{fig:fig_snr_comparison.png} shows the expected \ac{SNR}-Gain for the different noise signals and filter lengths. The result confirms, that a minimum filter length of about 32 taps is required, before (in any case) a significant rise in the \ac{SNR}-Gain can be observed - this is highly contrary to the synchrone intermediate high level simulation, where a filter length of only 16 taps provided sufficient noise reduction. This circumstance can be explained by the fact, that the corruption noise signal is now delayed to the reference noise signal, meaning, that the filter needs a certain length before it can be sufficiently adapted. The results also show, that the \ac{SNR}-Gain is different for the different noise signals, indicating, that the noise signal´s different characteristics, like the number and duration of peaks, propose differing challenges for the \ac{ANR} algorithm.\\ \\
 The mean \ac{SNR}-Gain of the different noise signals, also shown in Figure \ref{fig:fig_snr_comparison.png}, signals, that after reaching 95\% of the maximum \ac{SNR}-Gain, the \ac{SNR}-Gain increase is slowing down - that threshold is reached at a filter length of 45 taps. This means, that a filter length of 45 taps represents an optimal solution for a statisfying performance of the \ac{ANR} algorithm, while a further increase of the filter length does not lead to a significant increase of the \ac{SNR}-Gain in this setup. This is an important finding, as it allows optimizing the computational efficiency of the \ac{ANR} algorithm by choosing an appropriate filter length.
-\subsection{Evaluation of a Full-update implementation}
+\subsection{Evaluation of a full-update implementation}
 \noindent Equation \ref{equation_computing_final} can now be utilized to calculate the needed cycles for the calculation of one sample of the filter output, using a filter length of 45 taps and an update of the filter coefficients every cycle. The needed cycles are calculated as follows:
 \begin{equation}
 \label{equation_computing_calculation_full_update}
@@ -43,7 +43,7 @@ As already mentioned in the previous chapters, the sampling rate of the audio da
 \noindent The results, calculated in Equation \ref{equation_computing_calculation_full_update} to \ref{equation_load_calculation_full_update} can also be recapped as follows:\\ \\ 
 With the optimal filter length of 45 taps and an update rate of the filter coefficients every cycle, the \ac{ANR} algorithm is able to achieve a \ac{SNR}-Gain of about 11.54 dB, averaged over different signal/noise combinations. Under these circumstances, the computational load of the \ac{DSP} core amounts about 45\%, which means that 55\% of the time, which a new sample takes to arrive, it can be halted, and therefore, the overall power consumption can be reduced.\\ \\
 The initial signal/noise combination of a male speaker disturbed by a breathing noise, which is used for the verification of the \ac{DSP} implementation, shall serve as a benchmark for the coming evaluations. With 45 filter coefficients it delivers a \ac{SNR}-Gain of about 9.47 dB.
-\subsection{Evaluation of a Reduced-update implementation}
+\subsection{Evaluation of a reduced-update implementation}
 \subsubsection{Reduced-update implementation for the benchmark case}
 The most straight-forward method to further reduce the computing effort for the \ac{DSP} core is to reduce the update frequency of the filter coeffcients. For every sample, the new filter coefficients are calculated, but not written to the into the Filter Line - this means, that the filter, calculated for the previous sample, is applied to the actual sample. Depending on the acoustic situation, the savings in computing power will most likely lead to a degredation of the noise reduction quality, depending on if the current situation is highly dynamic (and therefore would require a frequent update of the filter coefficients) or is rather static. Changing the update frequency, changes the denominator in Equation \ref{equation_c_5} and therefore in Equation \ref{equation_computing_final}.\\ \\
 As already mentioned, the reduction of the update rate is initially evaluated for the benchmark case (male speaker disturbed by a breathing noise) and then checked for general validity. Therefore, the \ac{SNR}-Gain of 9.47 dB with 45 filter coefficients represent 100\% achievable noise reduction with a maximum of 357 cycles (also 100\%) in the following figure.