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main.aux
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\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces \textbf {Step-wise emergence of intensity-invariant song representation along the full 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 }
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\@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 }
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\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces \textbf {Step-wise emergence of intensity invariant song representation along the model pathway without logarithmic compression.} 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 }
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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 }
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\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces \textbf {Interspecific and intraspecific feature variability.} Average value $\mu _{f_i}$ of each feature $f_i(t)$ against its counterpart from a 2nd feature set based on a different input $x_{\text {raw}}(t)$. Each dot within a subplot represents a single feature $f_i(t)$. 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). Data is based on the analysis underlying Fig\,\ref {fig:pipeline_full}. \textbf {Lower triangular}:~Interspecific comparisons between single songs of different species. \textbf {Upper triangular}:~Intraspecific comparisons between different songs of a single species (\textit {O. rufipes}). \textbf {Lower right}:~Distribution of correlation coefficients $\rho $ for each interspecific and intraspecific comparison. Dots indicate single $\rho $ values. }}{29}{}\protected@file@percent }
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\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces \textbf {Intensity invariance along the model pathway in a naturalistic setting.} Input $x_{\text {raw}}(t)$ consists of a song of \textit {P. parallelus} recorded in the field at eight different distances $d$ 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}:~$x_{\text {filt}}(t)$, $x_{\text {env}}(t)$, $x_{\text {log}}(t)$, $x_{\text {adapt}}(t)$, $c_i(t)$, and $f_i(t)$ at each $d$. A noise segment from the same recording is shown for reference. \textbf {b}:~Intensity metrics over $d$. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. \textbf {c}:~Average value $\mu _{f_i}$ of each feature $f_i(t)$ over $d$. \textbf {d}:~Ratios of intensity metrics to the respective value obtained from the noise reference. 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 $\mu _{f_i}$. }}{28}{}\protected@file@percent }
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