diff --git a/main.aux b/main.aux
index 7fea7e7..30dae58 100644
--- a/main.aux
+++ b/main.aux
@@ -197,7 +197,7 @@
 \abx@aux@page{61}{5}
 \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces \textbf  {Schematic organisation of the grasshopper song recognition pathway and structure of the functional model pathway.} \textbf  {a}:~Simplified course of the pathway in the grasshopper, from the tympanal membrane over receptor neurons, local interneurons, and ascending neurons further towards the supraesophageal ganglion. \textbf  {b}:~Schematic of synaptic connections between the three neuronal populations within the metathoracic ganglion. \textbf  {c}:~Network representation of neuronal connectivity. \textbf  {d}:~Flow diagram of consecutive signal representations~(boxes) and transformations~(arrows) along the model pathway. All representations are time-varying. 1st half: Preprocessing stage~(one-dimensional representation). 2nd half: Feature extraction stage~(high-dimensional representation). }}{6}{}\protected@file@percent }
 \providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
-\newlabel{fig:pathway}{{1}{6}{\textbf  {Schematic organisation of the grasshopper song recognition pathway and structure of the functional model pathway.} \textbf  {a}:~Simplified course of the pathway in the grasshopper, from the tympanal membrane over receptor neurons, local interneurons, and ascending neurons further towards the supraesophageal ganglion. \textbf  {b}:~Schematic of synaptic connections between the three neuronal populations within the metathoracic ganglion. \textbf  {c}:~Network representation of neuronal connectivity. \textbf  {d}:~Flow diagram of consecutive signal representations~(boxes) and transformations~(arrows) along the model pathway. All representations are time-varying. 1st half: Preprocessing stage~(one-dimensional representation). 2nd half: Feature extraction stage~(high-dimensional representation). }{}{}}
+\newlabel{fig:pathway}{{1}{6}{}{}{}}
 \abx@aux@cite{0}{suga1960peripheral}
 \abx@aux@segm{0}{0}{suga1960peripheral}
 \abx@aux@cite{0}{gollisch2002energy}
@@ -224,7 +224,7 @@
 \abx@aux@page{69}{7}
 \newlabel{eq:highpass}{{4}{7}{}{}{}}
 \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces \textbf  {Representations of a song of \textit  {O. rufipes} during the preprocessing stage.} \textbf  {a}:~Bandpass filtered tympanal signal $x_{\text  {filt}}(t)$. \textbf  {b}:~Signal envelope $x_{\text  {env}}(t)$. \textbf  {c}:~Logarithmically compressed envelope $x_{\text  {log}}(t)$. \textbf  {d}:~Intensity-adapted envelope $x_{\text  {adapt}}(t)$. }}{8}{}\protected@file@percent }
-\newlabel{fig:stages_pre}{{2}{8}{\textbf  {Representations of a song of \textit  {O. rufipes} during the preprocessing stage.} \textbf  {a}:~Bandpass filtered tympanal signal $x_{\text  {filt}}(t)$. \textbf  {b}:~Signal envelope $x_{\text  {env}}(t)$. \textbf  {c}:~Logarithmically compressed envelope $x_{\text  {log}}(t)$. \textbf  {d}:~Intensity-adapted envelope $x_{\text  {adapt}}(t)$. }{}{}}
+\newlabel{fig:stages_pre}{{2}{8}{}{}{}}
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 \newlabel{eq:conv}{{5}{8}{}{}{}}
 \newlabel{eq:gabor}{{6}{8}{}{}{}}
@@ -235,77 +235,87 @@
 \abx@aux@cite{0}{bhavsar2017brain}
 \abx@aux@segm{0}{0}{bhavsar2017brain}
 \@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Values of phase $\phi $ that are specific for the four major groups of Gabor kernels.}}{9}{}\protected@file@percent }
-\newlabel{tab:gabor_phases}{{1}{9}{Values of phase $\phi $ that are specific for the four major groups of Gabor kernels.}{}{}}
+\newlabel{tab:gabor_phases}{{1}{9}{}{}{}}
 \newlabel{eq:binary}{{9}{9}{}{}{}}
 \abx@aux@page{70}{10}
 \abx@aux@page{71}{10}
 \abx@aux@page{72}{10}
 \newlabel{eq:lowpass}{{10}{10}{}{}{}}
 \@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces \textbf  {Representations of a song of \textit  {O. rufipes} during the feature extraction stage.} Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; two kernel widths $\sigma $ of $4\,$ms and $32\,$ms per type; 8 types, 16 kernels in total). \textbf  {a}:~Kernel-specific filter responses $c_i(t)$. \textbf  {b}:~Binary responses $b_i(t)$. \textbf  {c}:~Finalized features $f_i(t)$.}}{10}{}\protected@file@percent }
-\newlabel{fig:stages_feat}{{3}{10}{\textbf  {Representations of a song of \textit  {O. rufipes} during the feature extraction stage.} Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; two kernel widths $\sigma $ of $4\,$ms and $32\,$ms per type; 8 types, 16 kernels in total). \textbf  {a}:~Kernel-specific filter responses $c_i(t)$. \textbf  {b}:~Binary responses $b_i(t)$. \textbf  {c}:~Finalized features $f_i(t)$.}{}{}}
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-\@writefile{toc}{\contentsline {section}{\numberline {3}Results}{10}{}\protected@file@percent }
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-\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.1}Full-wave rectification \& lowpass filtering}{11}{}\protected@file@percent }
-\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf  {Rectification and lowpass filtering improves SNR but does not contribute to intensity invariance.} Input $x_{\text  {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is successively transformed into tympanal signal $x_{\text  {filt}}(t)$ and envelope $x_{\text  {env}}(t)$. Different line styles indicate different cutoff frequencies $f_{\text  {cut}}$ of the lowpass filter extracting $x_{\text  {env}}(t)$. \textbf  {Top}:~Example representations of $x_{\text  {filt}}(t)$ and $x_{\text  {env}}(t)$ for different $\alpha $. \textbf  {a}:~Noiseless case. \textbf  {b}:~Noisy case. \textbf  {Bottom}:~Intensity metrics over a range of $\alpha $. \textbf  {c}:~Noiseless case: Standard deviations $\sigma _x$ of $x_{\text  {filt}}(t)$ and $x_{\text  {env}}(t)$. \textbf  {d}:~Noisy case: Ratios of $\sigma _x$ of $x_{\text  {filt}}(t)$ and $x_{\text  {env}}(t)$ to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text  {raw}}(t)=\eta (t)$. \textbf  {e}:~Ratios of $\sigma _x$ to $\sigma _{\eta }$ of $x_{\text  {env}}(t)$ as in \textbf  {d} for different species (averaged over songs and recordings, see appendix Fig.\,\ref {fig:app_rect-lp}). }}{12}{}\protected@file@percent }
-\newlabel{fig:rect-lp}{{4}{12}{\textbf  {Rectification and lowpass filtering improves SNR but does not contribute to intensity invariance.} Input $x_{\text  {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is successively transformed into tympanal signal $x_{\text  {filt}}(t)$ and envelope $x_{\text  {env}}(t)$. Different line styles indicate different cutoff frequencies $f_{\text  {cut}}$ of the lowpass filter extracting $x_{\text  {env}}(t)$. \textbf  {Top}:~Example representations of $x_{\text  {filt}}(t)$ and $x_{\text  {env}}(t)$ for different $\alpha $. \textbf  {a}:~Noiseless case. \textbf  {b}:~Noisy case. \textbf  {Bottom}:~Intensity metrics over a range of $\alpha $. \textbf  {c}:~Noiseless case: Standard deviations $\sigma _x$ of $x_{\text  {filt}}(t)$ and $x_{\text  {env}}(t)$. \textbf  {d}:~Noisy case: Ratios of $\sigma _x$ of $x_{\text  {filt}}(t)$ and $x_{\text  {env}}(t)$ to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text  {raw}}(t)=\eta (t)$. \textbf  {e}:~Ratios of $\sigma _x$ to $\sigma _{\eta }$ of $x_{\text  {env}}(t)$ as in \textbf  {d} for different species (averaged over songs and recordings, see appendix Fig.\,\ref {fig:app_rect-lp}). }{}{}}
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-\newlabel{eq:toy_env_pure}{{11}{13}{}{}{}}
-\newlabel{eq:toy_log_pure}{{12}{13}{}{}{}}
-\newlabel{eq:toy_highpass_pure}{{13}{13}{}{}{}}
-\newlabel{eq:toy_env_noise}{{14}{13}{}{}{}}
-\newlabel{eq:toy_log_noise}{{15}{13}{}{}{}}
-\newlabel{eq:toy_highpass_noise}{{16}{14}{}{}{}}
-\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf  {Intensity invariance through logarithmic compression and adaptation is restricted by the noise floor and decreases SNR.} Input $x_{\text  {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is successively transformed into envelope $x_{\text  {env}}(t)$, logarithmically compressed envelope $x_{\text  {log}}(t)$, and intensity-adapted envelope $x_{\text  {adapt}}(t)$. \textbf  {Top}:~Example representations of $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, and $x_{\text  {adapt}}(t)$ for different $\alpha $. \textbf  {a}:~Noiseless case. \textbf  {b}:~Noisy case. \textbf  {Bottom}:~Intensity metrics over a range of $\alpha $. \textbf  {c}:~Noiseless case: Standard deviations $\sigma _x$ of $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, and $x_{\text  {adapt}}(t)$. \textbf  {d}:~Noisy case: Ratios of $\sigma _x$ of $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, and $x_{\text  {adapt}}(t)$ to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text  {filt}}(t)=\eta (t)$. Shaded areas indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey) curve span for $x_{\text  {adapt}}(t)$. \textbf  {e}:~Ratios of $\sigma _x$ to $\sigma _{\eta }$ of $x_{\text  {adapt}}(t)$ as in \textbf  {d} for different species (averaged over songs and recordings, see appendix Fig\,\ref {fig:app_log-hp_curves}). Dots indicate $95\,\%$ curve span per species. }}{15}{}\protected@file@percent }
-\newlabel{fig:log-hp}{{5}{15}{\textbf  {Intensity invariance through logarithmic compression and adaptation is restricted by the noise floor and decreases SNR.} Input $x_{\text  {filt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is successively transformed into envelope $x_{\text  {env}}(t)$, logarithmically compressed envelope $x_{\text  {log}}(t)$, and intensity-adapted envelope $x_{\text  {adapt}}(t)$. \textbf  {Top}:~Example representations of $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, and $x_{\text  {adapt}}(t)$ for different $\alpha $. \textbf  {a}:~Noiseless case. \textbf  {b}:~Noisy case. \textbf  {Bottom}:~Intensity metrics over a range of $\alpha $. \textbf  {c}:~Noiseless case: Standard deviations $\sigma _x$ of $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, and $x_{\text  {adapt}}(t)$. \textbf  {d}:~Noisy case: Ratios of $\sigma _x$ of $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, and $x_{\text  {adapt}}(t)$ to the respective reference standard deviation $\sigma _{\eta }$ for input $x_{\text  {filt}}(t)=\eta (t)$. Shaded areas indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey) curve span for $x_{\text  {adapt}}(t)$. \textbf  {e}:~Ratios of $\sigma _x$ to $\sigma _{\eta }$ of $x_{\text  {adapt}}(t)$ as in \textbf  {d} for different species (averaged over songs and recordings, see appendix Fig\,\ref {fig:app_log-hp_curves}). Dots indicate $95\,\%$ curve span per species. }{}{}}
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-\newlabel{eq:pdf_split}{{17}{16}{}{}{}}
-\newlabel{eq:feat_avg}{{18}{16}{}{}{}}
-\newlabel{eq:feat_prop}{{19}{16}{}{}{}}
-\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces \textbf  {Intensity invariance through thresholding and temporal averaging is mediated by the interaction of threshold value and noise floor.} Input $x_{\text  {adapt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is transformed into single kernel response $c(t)$, binary response $b(t)$, and feature $f(t)$. Different color shades indicate different threshold values $\Theta $ (multiples of reference standard deviation $\sigma _{\eta }$ of $c(t)$ for input $x_{\text  {adapt}}(t)=\eta (t)$, with darker colors for higher $\Theta $). \textbf  {Left}:~Noisy case: Example representations of $x_{\text  {adapt}}(t)$ as well as $c(t)$, $b(t)$, and $f(t)$ for different $\alpha $. \textbf  {a}:~$x_{\text  {adapt}}(t)$ with kernel $k(t)$ in black. \textbf  {b\,-\,d}: $c(t)$, $b(t)$, and $f(t)$ based on the same $x_{\text  {adapt}}(t)$ from \textbf  {a} but with different $\Theta $. \textbf  {Right}:~Average value $\mu _f$ of $f(t)$ for each $\Theta $ from \textbf  {b\,-\,d}. Dots indicate $95\,\%$ curve span (noisy case). \textbf  {e}:~$\mu _f$ over a range of $\alpha $, 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 $x_{\text  {adapt}}$ corresponding to the values of $\alpha $ shown in \textbf  {e}. Shaded area indicates standard deviations that would be capped in the output $x_{\text  {adapt}}(t)$ of the previous transformation pair (see Fig.\,\ref {fig:log-hp}cd). }}{18}{}\protected@file@percent }
-\newlabel{fig:thresh-lp_single}{{6}{18}{\textbf  {Intensity invariance through thresholding and temporal averaging is mediated by the interaction of threshold value and noise floor.} Input $x_{\text  {adapt}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with optional noise component $\eta (t)$ and is transformed into single kernel response $c(t)$, binary response $b(t)$, and feature $f(t)$. Different color shades indicate different threshold values $\Theta $ (multiples of reference standard deviation $\sigma _{\eta }$ of $c(t)$ for input $x_{\text  {adapt}}(t)=\eta (t)$, with darker colors for higher $\Theta $). \textbf  {Left}:~Noisy case: Example representations of $x_{\text  {adapt}}(t)$ as well as $c(t)$, $b(t)$, and $f(t)$ for different $\alpha $. \textbf  {a}:~$x_{\text  {adapt}}(t)$ with kernel $k(t)$ in black. \textbf  {b\,-\,d}: $c(t)$, $b(t)$, and $f(t)$ based on the same $x_{\text  {adapt}}(t)$ from \textbf  {a} but with different $\Theta $. \textbf  {Right}:~Average value $\mu _f$ of $f(t)$ for each $\Theta $ from \textbf  {b\,-\,d}. Dots indicate $95\,\%$ curve span (noisy case). \textbf  {e}:~$\mu _f$ over a range of $\alpha $, 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 $x_{\text  {adapt}}$ corresponding to the values of $\alpha $ shown in \textbf  {e}. Shaded area indicates standard deviations that would be capped in the output $x_{\text  {adapt}}(t)$ of the previous transformation pair (see Fig.\,\ref {fig:log-hp}cd). }{}{}}
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-\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces \textbf  {Feature representation of different species-specific songs saturates at different points in feature space.} Same input and processing as in Fig.\,\ref {fig:thresh-lp_single} but with three different kernels $k_i$, each with a single kernel-specific threshold value $\Theta _i=0.5\cdot \sigma _{\eta _i}$. \textbf  {a}:~Examples of species-specific grasshopper songs. \textbf  {Middle}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $ per species (averaged over songs and recordings, see appendix Figs.\,\ref {fig:app_thresh-lp_pure} and \ref {fig:app_thresh-lp_noise}). Different color shades indicate different kernels $k_i$. Dots indicate $95\,\%$ curve span per $k_i$. \textbf  {b}:~Noiseless case. \textbf  {c}:~Noisy case. \textbf  {Bottom}:~2D feature spaces spanned by each pair of $f_i(t)$. Each trajectory corresponds to a species-specific combination of $\mu _{f_i}$ that develops with $\alpha $ (colorbars). Horizontal dashes in the colorbar indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey) curve span of the norm across all three $\mu _{f_i}$ per species. \textbf  {d}:~Noiseless case. \textbf  {e}:~Noisy case. Shaded areas indicate the average minimum $\mu _{f_i}$ across all species-specific trajectories. }}{21}{}\protected@file@percent }
-\newlabel{fig:thresh-lp_species}{{7}{21}{\textbf  {Feature representation of different species-specific songs saturates at different points in feature space.} Same input and processing as in Fig.\,\ref {fig:thresh-lp_single} but with three different kernels $k_i$, each with a single kernel-specific threshold value $\Theta _i=0.5\cdot \sigma _{\eta _i}$. \textbf  {a}:~Examples of species-specific grasshopper songs. \textbf  {Middle}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $ per species (averaged over songs and recordings, see appendix Figs.\,\ref {fig:app_thresh-lp_pure} and \ref {fig:app_thresh-lp_noise}). Different color shades indicate different kernels $k_i$. Dots indicate $95\,\%$ curve span per $k_i$. \textbf  {b}:~Noiseless case. \textbf  {c}:~Noisy case. \textbf  {Bottom}:~2D feature spaces spanned by each pair of $f_i(t)$. Each trajectory corresponds to a species-specific combination of $\mu _{f_i}$ that develops with $\alpha $ (colorbars). Horizontal dashes in the colorbar indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey) curve span of the norm across all three $\mu _{f_i}$ per species. \textbf  {d}:~Noiseless case. \textbf  {e}:~Noisy case. Shaded areas indicate the average minimum $\mu _{f_i}$ across all species-specific trajectories. }{}{}}
-\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Intensity invariance along the full model pathway}{22}{}\protected@file@percent }
-\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.1}Including logarithmic compression}{22}{}\protected@file@percent }
-\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf  {Step-wise emergence of intensity-invariant song representations along the model pathway.} Input $x_{\text  {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf  {a}:~Example representations of $x_{\text  {filt}}(t)$, $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, $x_{\text  {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf  {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $x_{\text  {log}}(t)$, $x_{\text  {adapt}}(t)$, $c_i(t)$, and $f_i(t)$. \textbf  {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf  {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text  {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf  {e}:~Ratios of standard deviation $\sigma _{c_i}$ of each $c_i(t)$. \textbf  {f}:~Ratios of $\mu _{f_i}$. \textbf  {g}:~Distributions of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $c_i(t)$ and $f_i(t)$. Dots indicate the values from \textbf  {b}. }}{24}{}\protected@file@percent }
-\newlabel{fig:pipeline_full}{{8}{24}{\textbf  {Step-wise emergence of intensity-invariant song representations along the model pathway.} Input $x_{\text  {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf  {a}:~Example representations of $x_{\text  {filt}}(t)$, $x_{\text  {env}}(t)$, $x_{\text  {log}}(t)$, $x_{\text  {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf  {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $x_{\text  {log}}(t)$, $x_{\text  {adapt}}(t)$, $c_i(t)$, and $f_i(t)$. \textbf  {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf  {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text  {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf  {e}:~Ratios of standard deviation $\sigma _{c_i}$ of each $c_i(t)$. \textbf  {f}:~Ratios of $\mu _{f_i}$. \textbf  {g}:~Distributions of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $c_i(t)$ and $f_i(t)$. Dots indicate the values from \textbf  {b}. }{}{}}
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-\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf  {Effects of disabling logarithmic compression on intensity invariance along the model pathway.} Input $x_{\text  {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$, skipping $x_{\text  {log}}(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf  {a}:~Example representations of $x_{\text  {filt}}(t)$, $x_{\text  {env}}(t)$, $x_{\text  {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf  {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $f_i(t)$. \textbf  {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf  {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text  {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf  {e}:~Ratios of $\mu _{f_i}$. \textbf  {f}:~Distribution of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $f_i(t)$. Dots indicate the value from \textbf  {b}. }}{26}{}\protected@file@percent }
-\newlabel{fig:pipeline_short}{{9}{26}{\textbf  {Effects of disabling logarithmic compression on intensity invariance along the model pathway.} Input $x_{\text  {raw}}(t)$ consists of song component $s(t)$ scaled by $\alpha $ with added noise component $\eta (t)$ and is processed up to the feature set $f_i(t)$, skipping $x_{\text  {log}}(t)$. Different color shades indicate different types of Gabor kernels with specific lobe number $n$ and either $+$ or $-$ sign, sorted (dark to light) first by increasing $n$ and then by sign~($1\,\leq \,n\,\leq \,4$; first $+$, then $-$ for each $n$; five kernel widths $\sigma $ of 1, 2, 4, 8, and $16\,$ms per type; 8 types, 40 kernels in total). \textbf  {a}:~Example representations of $x_{\text  {filt}}(t)$, $x_{\text  {env}}(t)$, $x_{\text  {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ for different $\alpha $. \textbf  {b}:~Intensity metrics over $\alpha $. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $f_i(t)$. \textbf  {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $\alpha $. \textbf  {d}:~Ratios of intensity metrics to the respective reference value for input $x_{\text  {raw}}(t)=\eta (t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. \textbf  {e}:~Ratios of $\mu _{f_i}$. \textbf  {f}:~Distribution of kernel-specific $\alpha $ that correspond to $95\,\%$ curve span for $f_i(t)$. Dots indicate the value from \textbf  {b}. }{}{}}
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-[768] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0
-[870] bibtex.pm:1713> INFO - LaTeX decoding ...
-[1069] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
-[1720] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
-[1720] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
-[1722] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
-[1722] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
-[1860] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8'
-[1942] bbl.pm:763> INFO - Output to main.bbl
-[1942] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 10, warning: 6 characters of junk seen at toplevel
-[1942] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 21, warning: 6 characters of junk seen at toplevel
-[1942] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 38, warning: 6 characters of junk seen at toplevel
-[1943] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 49, warning: 6 characters of junk seen at toplevel
-[1943] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 58, warning: 6 characters of junk seen at toplevel
-[1943] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 73, warning: 6 characters of junk seen at toplevel
-[1943] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 82, warning: 6 characters of junk seen at toplevel
-[1943] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 91, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 100, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 109, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 118, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 127, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 136, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 157, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 178, warning: 6 characters of junk seen at toplevel
-[1944] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 187, warning: 6 characters of junk seen at toplevel
-[1945] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 196, warning: 6 characters of junk seen at toplevel
-[1945] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 207, warning: 6 characters of junk seen at toplevel
-[1945] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 218, warning: 6 characters of junk seen at toplevel
-[1945] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 229, warning: 6 characters of junk seen at toplevel
-[1945] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 240, warning: 6 characters of junk seen at toplevel
-[1945] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 249, warning: 6 characters of junk seen at toplevel
-[1945] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 258, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 269, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 278, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 289, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 300, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 309, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 328, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 337, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 400, warning: 6 characters of junk seen at toplevel
-[1946] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 419, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 428, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 437, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 456, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 491, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 526, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 535, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 556, warning: 6 characters of junk seen at toplevel
-[1947] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 565, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 576, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 587, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 619, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 648, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 658, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 667, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 688, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 709, warning: 6 characters of junk seen at toplevel
-[1948] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 720, warning: 6 characters of junk seen at toplevel
-[1949] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 729, warning: 6 characters of junk seen at toplevel
-[1949] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 749, warning: 6 characters of junk seen at toplevel
-[1949] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 766, warning: 6 characters of junk seen at toplevel
-[1949] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 775, warning: 6 characters of junk seen at toplevel
-[1949] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 800, warning: 6 characters of junk seen at toplevel
-[1949] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_IYfb\347c261ec4135a5723bef5c751f5078f_15764.utf8, line 817, warning: 6 characters of junk seen at toplevel
-[1949] Biber.pm:133> INFO - WARNINGS: 55
+[37] biber:340> INFO - === Mi Mai 13, 2026, 16:57:56
+[46] Biber.pm:419> INFO - Reading 'main.bcf'
+[74] Biber.pm:979> INFO - Found 55 citekeys in bib section 0
+[80] Biber.pm:4419> INFO - Processing section 0
+[85] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0
+[87] bibtex.pm:1713> INFO - LaTeX decoding ...
+[117] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib'
+[302] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
+[302] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
+[302] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
+[302] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
+[327] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8'
+[338] bbl.pm:763> INFO - Output to main.bbl
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 10, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 21, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 38, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 49, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 58, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 73, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 82, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 91, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 100, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 109, warning: 6 characters of junk seen at toplevel
+[338] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 118, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 127, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 136, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 157, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 178, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 187, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 196, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 207, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 218, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 229, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 240, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 249, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 258, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 269, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 278, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 289, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 300, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 309, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 328, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 337, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 400, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 419, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 428, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 437, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 456, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 491, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 526, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 535, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 556, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 565, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 576, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 587, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 619, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 648, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 658, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 667, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 688, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 709, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 720, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 729, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 749, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 766, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 775, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 800, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_XHlH/347c261ec4135a5723bef5c751f5078f_39942.utf8, line 817, warning: 6 characters of junk seen at toplevel
+[339] Biber.pm:133> INFO - WARNINGS: 55
diff --git a/main.fdb_latexmk b/main.fdb_latexmk
index 32d6965..a811842 100644
--- a/main.fdb_latexmk
+++ b/main.fdb_latexmk
@@ -1,182 +1,187 @@
 # Fdb version 4
-["biber main"] 1778525341 "main.bcf" "main.bbl" "main" 1778525580 0
-  "./cite.bib" 1771664051 28308 4290db0c91f7b5055e25472ef913f6b4 ""
-  "main.bcf" 1778525580 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
+["biber main"] 1778684276.42701 "main.bcf" "main.bbl" "main" 1778684738.79505 0
+  "cite.bib" 1770904753.08918 27483 4290db0c91f7b5055e25472ef913f6b4 ""
+  "main.bcf" 1778684738.72627 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
   (generated)
   "main.bbl"
   "main.blg"
   (rewritten before read)
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 Package siunitx Info: Option "separate-uncertainty" has been deprecated in
 (siunitx)             this release.
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 Package biblatex Info: Trying to load biblatex default data model...
 Package biblatex Info: ... file 'blx-dm.def' found.
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 Package biblatex Info: Trying to load biblatex custom data model...
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 Package biblatex Info: Trying to load compatibility code...
 Package biblatex Info: ... file 'blx-compat.def' found.
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 File: blx-compat.def 2023/03/05 v3.19 biblatex compatibility (PK/MW)
 )
 Package biblatex Info: Trying to load generic definitions...
 Package biblatex Info: ... file 'biblatex.def' found.
- (C:\Users\jonah\AppData\Local\Programs\MiKTeX\tex/latex/biblatex\biblatex.def
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 File: biblatex.def 2023/03/05 v3.19 biblatex compatibility (PK/MW)
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 Package biblatex Info: Trying to load bibliography style 'authoryear'...
 Package biblatex Info: ... file 'authoryear.bbx' found.
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 File: authoryear.bbx 2023/03/05 v3.19 biblatex bibliography style (PK/MW)
 Package biblatex Info: Trying to load bibliography style 'standard'...
 Package biblatex Info: ... file 'standard.bbx' found.
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 Package biblatex Info: Trying to load citation style 'authoryear'...
 Package biblatex Info: ... file 'authoryear.cbx' found.
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 File: authoryear.cbx 2023/03/05 v3.19 biblatex citation style (PK/MW)
 Package biblatex Info: Redefining '\cite'.
 Package biblatex Info: Redefining '\parencite'.
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 )
 Package biblatex Info: Trying to load configuration file...
 Package biblatex Info: ... file 'biblatex.cfg' found.
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 Package biblatex Info: Input encoding 'utf8' detected.
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 Package biblatex Info: ... and expl3
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 Package: blx-case-expl3 2023/03/05 v3.19 expl3 case changing code for biblatex
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 Package biblatex Warning: 'babel/polyglossia' detected but 'csquotes' missing.
 (biblatex)                Loading 'csquotes' recommended.
 
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 LaTeX Font Info:    Checking defaults for OML/cmm/m/it on input line 40.
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 Package: epstopdf-base 2020-01-24 v2.11 Base part for package epstopdf
 Package epstopdf-base Info: Redefining graphics rule for `.eps' on input line 485.
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 Package caption Info: Begin \AtBeginDocument code.
 Package caption Info: End \AtBeginDocument code.
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 File: translations-basic-dictionary-english.trsl (english translation file `translations-basic-dictionary')
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 Package translations Info: loading dictionary `translations-basic-dictionary' for `english'. on input line 40.
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 File: translations-basic-dictionary-german.trsl (german translation file `translations-basic-dictionary')
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 Package translations Info: loading dictionary `translations-basic-dictionary' for `german'. on input line 40.
 Package biblatex Info: Trying to load language 'english'...
 Package biblatex Info: ... file 'english.lbx' found.
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 File: english.lbx 2023/03/05 v3.19 biblatex localization (PK/MW)
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 Package biblatex Info: Trying to load language 'german'...
 Package biblatex Info: ... file 'german.lbx' found.
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 Package biblatex Info: Input encoding 'utf8' detected.
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 Package biblatex Info: Trying to load bibliographic data...
 Package biblatex Info: ... file 'main.bbl' found.
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 Package biblatex Info: Reference section=0 on input line 40.
 Package biblatex Info: Reference segment=0 on input line 40.
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 LaTeX Font Info:    Trying to load font information for U+msb on input line 41.
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diff --git a/main.pdf b/main.pdf
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diff --git a/main.synctex.gz b/main.synctex.gz
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diff --git a/main.tex b/main.tex
index 6c3b340..87450ba 100644
--- a/main.tex
+++ b/main.tex
@@ -103,6 +103,9 @@
 \newcommand{\xvar}{\sigma_{x}^{2}} % Variance of synthetic mixture
 \newcommand{\svar}{\sigma_{\text{s}}^{2}} % Song component variance
 \newcommand{\nvar}{\sigma_{\eta}^{2}} % Noise component variance
+\newcommand{\xsig}{\sigma_x} % Standard deviation of synthetic mixture
+\newcommand{\ssig}{\sigma_{\text{s}}} % Song component standard deviation
+\newcommand{\nsig}{\sigma_{\eta}} % Noise component standard deviation
 \newcommand{\pc}{p(c,\,T)} % Probability density (general interval)
 \newcommand{\pclp}{p(c,\,\tlp)} % Probability density (lowpass interval)
 \newcommand{\muf}{\mu_{f_i}} % Average feature value
@@ -558,7 +561,170 @@ can be read out by a simple linear classifier.
 \end{figure}
 \FloatBarrier
 
-\subsubsection{Simulation-based analysis of the model pathway}
+\subsection{Simulation-based analysis of the model pathway}
+
+\subsubsection{Data sourcing}
+
+All simulations were based on a dataset that was assembled from five different
+sources, each of which is an established reference for the identification of
+European grasshopper species. The dataset was limited to six species from the
+species-rich \textit{Gomphocerinae} sub-family that are known to be common
+throughout Central and Southern Europe. All recordings were converted to
+standard~\textit{.wav}~format with a sampling rate of~44.1\,kHz and an
+amplitude scale in arbitrary units. Individual songs were then cut from each
+recording. The dataset includes a total of 31 recordings across species, which
+amounts to a total of 153 isolated songs. However, the number of available
+species-specific songs varies greatly across species, with a maximum of 48
+songs for \textit{C. biguttulus} and a minimum of 6 songs for \textit{C.
+mollis}~(Tab.\,\ref{tab:species_list}).
+
+\begin{itemize}
+    \item "Heuschrecken beobachten, bestimmen" by~Heiko~Bellmann\\
+    1$^{\text{st}}$\,edition, 1993, Naturbuch, Augsburg
+    \item "Gesänge der heimischen Heuschrecken. Akustisch-optische
+    Bestimmungshilfe."\\
+    by~Karl-Heinz~Garberding, Deutscher Jugendbund für Naturbeobachtung\\
+    1$^{\text{st}}$\,edition, 2001, DJN, Göttingen
+    \item "Heuschrecken -- Die Stimmen von 61 heimischen Arten"
+    by~Heiko~Bellmann\\
+    1$^{\text{st}}$\,edition, 2004, AMPLE, Germering
+    \item "Fauna d'Italia XLVIII -- Orthoptera" by~Bruno~Massa, Paolo~Fontana,
+    Filippo~M.~Buzzetti, Roy~M.J.C.~Kleukers, Baudewijn~Odé\\
+    1$^{\text{st}}$\,edition, 2012, edagricola, Milano
+    \item "Singing Orthoptera of Slovenia" by~Stanislav~Gomboc, Blaz~Segula\\
+    1$^{\text{st}}$\,edition, 2014, EGEA, Ljubljana
+\end{itemize}
+
+\begin{table}[!ht]
+    \centering
+    \captionsetup{width=.75\textwidth}
+    \caption{Overview of the six grasshopper species from the
+    \textit{Gomphocerinae} sub-family, the number of sources per species, the
+    number of available recordings across sources, and the number of isolated
+    songs across recordings.}
+    \begin{tabular}{|lccc|}
+        \hline
+        \textbf{Species} & \textbf{Sources} & \textbf{Recordings} & \textbf{Songs}\\
+        \hline
+        \textit{Chorthippus biguttulus} & 5 & 6 & 48\\ 
+        \textit{Chorthippus mollis} & 3 & 3 & 6\\ 
+        \textit{Chrysochraon dispar} & 4 & 5 & 45\\ 
+        \textit{Gomphocerippus rufus} & 4 & 8 &  16\\ 
+        \textit{Omocestus rufipes} & 4 & 5 & 14\\ 
+        \textit{Pseudochorthippus parallelus} & 4 & 4 & 24\\ 
+        \hline
+    \end{tabular}
+    \label{tab:species_list}
+\end{table}
+
+\subsubsection{Generating synthetic input signals}
+
+Different processing steps along the model pathway were tested for intensity
+invariance by generating synthetic input signals $x(t)$ of varying intensity,
+transforming them through the respective processing steps, and comparing the
+resulting signal representations. Inputs were generated for two distinct cases.
+In the idealized, noiseless case, $x(t)$ consists of a song component $\soc(t)$
+with $\ssig=1$ and a multiplicative scale $\sca$:
+\begin{equation}
+    x(t)\,=\,\sca\,\cdot\,\soc(t), \qquad \sca\,\geq\,0
+    \label{eq:noiseless}
+\end{equation}
+In the noiseless case, $x(t)$ is hence only a scaled version of $\soc(t)$ with
+$\xsig=\sca$. In the more realistic, noisy case, $x(t)$ consists of the same
+song component $\soc(t)$ scaled by $\sca$ and an additive noise component
+$\noc(t)$ with $\nsig=1$:
+\begin{equation}
+    x(t)\,=\,\sca\,\cdot\,\soc(t)\,+\,\noc(t), \qquad \sca\,\geq\,0
+    \label{eq:noisy}
+\end{equation}
+Accordingly, the SNR of input $x(t)$ in the noisy case equals the squared
+$\sca$ value:
+% Make sure that SNR = signal-to-noise ratio is introduced somewhere!
+\begin{equation}
+    \text{SNR}_x(\sca)\,=\,\frac{(\sca\,\cdot\,\ssig)^2}{\nsig^2}\,=\,\sca^2, \qquad \ssig\,=\,\nsig\,=\,1
+    \label{eq:input_snr}
+\end{equation}
+For most analyses, it would be sufficient if input $x(t)$ corresponds to the
+signal representation immediately before the first of the tested
+transformations. For instance, when testing the effects of logarithmic
+compression~(Eq.\,\ref{eq:log}), $x(t)$ would correspond to the signal envelope
+$\env(t)$. However, in this particular case, $\env(t)$ results from a nonlinear
+transformation~(Eq.\,\ref{eq:env}), which cannot be synthesized as an additive
+mixture of $\soc(t)$ and $\noc(t)$. For this reason, any input $x(t)$ across
+all analyses corresponds not to the representation immediately before the
+tested transformations but its predecessor representation instead. Therefore,
+when testing logarithmic compression, $x(t)$ corresponds to the tympanal signal
+$\filt(t)$ instead of $\env(t)$.
+
+The raw $\soc(t)$ was drawn from the dataset of isolated species-specific song
+recordings, whereas the raw $\noc(t)$ consists of a segment of normally
+distributed white noise. Both $\soc(t)$ and $\noc(t)$ were normalized to unit
+standard deviation. These can be used without further processing for all
+analyses where input $x(t)$ corresponds to $\raw(t)$. For analyses where $x(t)$
+corresponds to a later representation, $\soc(t)$ and $\noc(t)$ were first
+processed along the model pathway up to the required representation, again
+normalized to unit standard deviation, and then used to generate $x(t)$
+according to either Eq.\,\ref{eq:noiseless} in the noiseless case or
+Eq.\,\ref{eq:noisy} in the noisy case.
+
+\subsubsection{Quantifying signal intensity across representations}
+
+All intensity measures were calculated over a manually labeled segment within
+each song. Segments always excluded the first and last few syllables to allow
+slowly changing representations such as $f_i(t)$ to stabilize. The duration of
+each segment and the number of contained syllables depends on the duration of
+the species-specific song. Care was taken to ensure that the segment contained
+a sufficient number of syllables to obtain a reliable estimate of the intensity
+measures.
+
+The standard deviation $\sigma$ was used as a measure of intensity for all
+representations resulting from the transformation of input $x(t)$ up to and
+including the kernel responses $c_i(t)$, for which individual $\sigma_{c_i}$
+were used as kernel-specific intensity measures. The binary responses $b_i(t)$
+were deemed to similar to the features $f_i(t)$ to warrant their own intensity
+measure and were hence omitted from all related analyses. For $f_i(t)$,
+$\sigma$ is not an appropriate intensity measure because each $f_i(t)$ is
+ideally constant with $\sigma=0$ for the duration of a song. Therefore, the
+average value $\muf$ of each $f_i(t)$ was used as a kernel-specific intensity
+measure instead.
+
+It is arguably not ideal to quantify the intensity of $c_i(t)$ and $f_i(t)$
+separately for each kernel. Overall, these representations are not separate
+signals bundled together but rather a set that acts as a unit with a single
+intensity measure. However, there is no straightforward way to quantify the
+intensity of $c_i(t)$ or $f_i(t)$ as a whole that would not entail a certain
+ambiguity, e.\,g by averaging across kernels. In this sense, we opted for the
+kernel-specific approach because it allows to asses differences in the
+dependency on $\sca$ between individual members of either $c_i(t)$ and
+$f_i(t)$.
+
+The absolute intensity measures allow to compare the intensity of a
+representation across different $\sca$ values. Additionally, ratios were
+calculated between the intensity measures for $\sca>0$ and the respective
+pure-noise reference measure for $\sca=0$ to better compare the intensities of
+different representations. This is only possible in the noisy case, where input
+$x(t)=\noc(t)$ for $\sca=0$, whereas $x(t)=0$ for $\sca=0$ in the noiseless
+case. At the level of input $x(t)$, the ratio of intensity measures depends on
+the square root of $\sca$:
+\begin{equation}
+    \frac{\xsig}{\nsig}\,=\,\sqrt{\frac{\xsig^2}{\nsig^2}}\,=\,\sqrt{\frac{(\sca\,\cdot\,\ssig)^2\,+\,\nsig^2}{\nsig^2}}\,=\,\sqrt{\sca^2\,+\,1}, \qquad \ssig\,=\,\nsig\,=\,1
+    \label{eq:input_ratio}
+\end{equation}
+This holds only if $\soc(t)\perp\noc(t)$, so that $\xsig^2=\ssig^2+\nsig^2$,
+which is a reasonable assumption for the raw $\soc(t)$ and $\noc(t)$. However,
+the dependency of the ratio on $\sca$ is not necessarily the same for
+representations that are transformed from $x(t)$ by nonlinear operations, since
+these change the relationship of $\soc(t)$ and $\noc(t)$ in an unpredictable
+fashion. Furthermore, the ratio is not a proper SNR of the representation
+because it does not relate $\soc(t)$ to $\noc(t)$ within the representation but
+rather the entire representation to $\noc(t)$ alone. However, it still provides
+a useful measure of the relative intensity of a representation with and without
+$\soc(t)$, which is the closest we can get to the SNR of the representation. As
+such, the ratio of intensity measures is referred to as SNR in the following.
+% Is this legal? "SNR" is much shorter than "ratio of intensity measure to the pure-noise reference measure".
+% Haven't used it much yet, sticked to "ratio" in most cases.
+
+\subsection{Field data-based analysis of the model pathway}
 
 \section{Results}
 
diff --git a/python/count_species_songs.py b/python/count_species_songs.py
new file mode 100644
index 0000000..3576ec8
--- /dev/null
+++ b/python/count_species_songs.py
@@ -0,0 +1,41 @@
+import json
+from thunderhopper.filetools import search_files
+from misc_functions import sort_files_by_rec
+from IPython import embed
+
+# GENERAL SETTINGS:
+target_species = [
+    'Chorthippus_biguttulus',
+    'Chorthippus_mollis',
+    'Chrysochraon_dispar',
+    'Gomphocerippus_rufus',
+    'Omocestus_rufipes',
+    'Pseudochorthippus_parallelus',
+]
+search_kwargs = dict(
+    ext='.wav',
+    dir='../data/raw/',
+)
+sources = ['BM04', 'BM93', 'DJN', 'GBC', 'FTN']
+
+# EXECUTION:
+n_songs, n_recs, n_sources = {}, {}, {}
+for species in target_species:
+    print(f'\nCounting {species} songs...')
+
+    # Fetch all species-specific song files:
+    paths = search_files(species, **search_kwargs)
+
+    # Sort song files by recording:
+    sorted_paths = sort_files_by_rec(paths, sources, return_dict=True)
+
+    # Count number of available sources:
+    n_sources[species] = len(sorted_paths)
+
+    # Count number of available recordings:
+    n_recs[species] = sum(len(rec_paths) for rec_paths in sorted_paths.values())
+
+    # Count number of available songs:
+    n_songs[species] = sum(sum(len(paths) for paths in rec_paths) for rec_paths in sorted_paths.values())
+
+    print(f'Found {n_songs[species]} songs from {n_recs[species]} recordings across {n_sources[species]} sources.')
\ No newline at end of file
diff --git a/python/misc_functions.py b/python/misc_functions.py
index df0cbb3..2748314 100644
--- a/python/misc_functions.py
+++ b/python/misc_functions.py
@@ -45,7 +45,8 @@ def reduce_kernel_set(data, inds, keys=None, combis=None):
             data[key] = data[key][:, inds, ...]
     return data
 
-def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
+def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN'],
+                      return_dict=False):
     # Separate by source:
     sorted_paths = {}
     for source in sources:
@@ -73,7 +74,11 @@ def sort_files_by_rec(paths, sources=['BM04', 'BM93', 'DJN', 'GBC', 'FTN']):
                 # Open new recording-specific slot:
                 sorted_paths[source].append([])
             sorted_paths[source][sub_id - 1].append(path)
-    
+
+    # Keep source separation:
+    if return_dict:
+        return sorted_paths
+
     # Re-sort song files by recording only (discarding source separation):
     sorted_paths = [path for paths in sorted_paths.values() for path in paths]
     return sorted_paths