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@@ -610,16 +610,16 @@ This effect is more pronounced for lower $\fc$ of the lowpass filter and is
 presumably caused by the attenuation of high-frequency components in the
 signal, which are more prominent in the noise component $\noc(t)$ than in the
 song component $\soc(t)$. The effect also appears relatively consistent across
-different species, although small variations based on different song structures
-and distributions exist~(Fig.\,\ref{fig:rect-lp}e). In summary, the standard
-deviation of $\env(t)$ has never been observed to transition into a saturation
-regime for larger $\sca$ but rather continues to increase proportionally to
-$\sca$ for all tested $\fc$, in both the noiseless and the noisy case and
-across different species. Consequently, the combination of rectification and
-lowpass filtering does not contribute to intensity invariance. However, this
-transformation pair does improve the SNR of $\env(t)$ relative to $\filt(t)$
-and thus provides subsequent processing stages with a more robust input
-representation and higher input SNR.
+different species, although small variations exist~(Fig.\,\ref{fig:rect-lp}e)
+that are presumably based on different song structures and frequency spectra.
+In summary, the standard deviation of $\env(t)$ has never been observed to
+transition into a saturation regime for larger $\sca$ but rather continues to
+increase proportionally to $\sca$ for all tested $\fc$, in both the noiseless
+and the noisy case and across different species. Consequently, the combination
+of rectification and lowpass filtering does not contribute to intensity
+invariance. However, this transformation pair does improve the SNR of $\env(t)$
+relative to $\filt(t)$ and thus provides subsequent processing stages with a
+more robust input representation and higher input SNR.
 
 \begin{figure}[!ht]
     \centering
@@ -880,7 +880,21 @@ the SNR of $\adapt(t)$ are much less understood and likely relate to properties
 of the signal, whereas the SNR of $f(t)$ depends on the choice of $\Theta$ and
 can be more directly manipulated by the system.
 
-Finally, 
+Finally, the effects of thresholding and temporal averaging must be seen in the
+context of the previous transformation pair of logarithmic compression and
+adaptation. 
+
+Finally, the question remains whether the intensity-invariant output $\adapt(t)$
+of the previous transformation pair allows feature 
+
+Finally, the output $\adapt(t)$ of the previous transformation
+pair~(Fig.\,\ref{fig:log-hp}cd) can be related to the input $\adapt(t)$ of the
+current transformation pair by plotting the values of $f(t)$ over the standard
+deviation of input $\adapt(t)$ instead of
+$\sca$~(Fig.\,\ref{fig:thresh-lp_single}f). This is relevant because, unlike
+$\sca$, the standard deviation of $\adapt(t)$ is capped to a maximum value of
+around 10\,dB by the previous transformation pair~(Fig.\,\ref{fig:log-hp}cd)
+
 
 \begin{figure}[!ht]
     \centering
@@ -904,14 +918,17 @@ Finally,
                      same $\adapt(t)$ from \textbf{a} but with different
                      $\Theta$.
                      \textbf{Right}:~Average value $\mu_f$ of $f(t)$ for each
-                     $\Theta$ from \textbf{b\,-\,d}, once for the noisy case
-                     (solid lines) and once for the noiseless case (dotted
-                     lines). Dots indicate $95\,\%$ curve span (noisy case).
-                     \textbf{e}:~$\mu_f$ over a range of $\sca$.
-                     \textbf{f}:~$\mu_f$ over the standard deviation of noisy
-                     input $\adapt$ corresponding to the values of $\sca$ shown
-                     in \textbf{e}.
-                     % Why plot noiseless case over SD of noisy input? Omit?
+                     $\Theta$ from \textbf{b\,-\,d}. Dots indicate $95\,\%$
+                     curve span (noisy case).
+                     \textbf{e}:~$\mu_f$ over a range of $\sca$, once for the
+                     noisy case (solid lines) and once for the noiseless case
+                     (dotted lines).
+                     \textbf{f}:~Noisy case: $\mu_f$ over the standard
+                     deviation of input $\adapt$ corresponding to the values of
+                     $\sca$ shown in \textbf{e}. Shaded area indicates standard
+                     deviations that would be capped in the output $\adapt(t)$
+                     of the previous transformation pair (see
+                     Fig.\,\ref{fig:log-hp}cd).
                      }
     \label{fig:thresh-lp_single}
 \end{figure}