diff --git a/main.aux b/main.aux index 30dae58..063a941 100644 --- a/main.aux +++ b/main.aux @@ -173,7 +173,6 @@ \abx@aux@page{49}{4} \abx@aux@page{50}{4} \@writefile{toc}{\contentsline {section}{\numberline {2}Methods}{4}{}\protected@file@percent } -\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Functional model of the grasshopper song recognition pathway}{4}{}\protected@file@percent } \abx@aux@cite{0}{windmill2008time} \abx@aux@segm{0}{0}{windmill2008time} \abx@aux@cite{0}{malkin2014energy} @@ -182,6 +181,7 @@ \abx@aux@segm{0}{0}{machens2001discrimination} \abx@aux@cite{0}{machens2001representation} \abx@aux@segm{0}{0}{machens2001representation} +\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Functional model of the grasshopper song recognition pathway}{5}{}\protected@file@percent } \abx@aux@page{51}{5} \abx@aux@page{52}{5} \abx@aux@page{53}{5} @@ -194,7 +194,6 @@ \abx@aux@page{59}{5} \abx@aux@page{60}{5} \newlabel{eq:bandpass}{{1}{5}{}{}{}} -\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}{}{}{}} @@ -212,6 +211,7 @@ \abx@aux@segm{0}{0}{hildebrandt2009origin} \abx@aux@cite{0}{clemens2010intensity} \abx@aux@segm{0}{0}{clemens2010intensity} +\abx@aux@page{61}{7} \abx@aux@page{62}{7} \newlabel{eq:env}{{2}{7}{}{}{}} \abx@aux@page{63}{7} @@ -247,17 +247,18 @@ \@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.1}Data sourcing}{10}{}\protected@file@percent } \@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces 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.}}{11}{}\protected@file@percent } \newlabel{tab:species_list}{{2}{11}{}{}{}} -\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.2}Generating synthetic input signals}{11}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.2}Generation of synthetic input signals}{11}{}\protected@file@percent } \newlabel{eq:noiseless}{{11}{11}{}{}{}} \newlabel{eq:noisy}{{12}{12}{}{}{}} \newlabel{eq:input_snr}{{13}{12}{}{}{}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.3}Quantifying signal intensity across representations}{12}{}\protected@file@percent } +\newlabel{sec:intensity_measures}{{2.2.3}{12}{}{}{}} \newlabel{eq:input_ratio}{{14}{13}{}{}{}} -\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}Field data-based analysis of the model pathway}{14}{}\protected@file@percent } +\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}Field data-based analysis of the model pathway}{13}{}\protected@file@percent } \@writefile{toc}{\contentsline {section}{\numberline {3}Results}{14}{}\protected@file@percent } \@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Mechanisms driving the emergence of intensity invariance}{14}{}\protected@file@percent } \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.1}Full-wave rectification \& lowpass filtering}{14}{}\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}). }}{16}{}\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 measures 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}). }}{16}{}\protected@file@percent } \newlabel{fig:rect-lp}{{4}{16}{}{}{}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.2}Logarithmic compression \& spike-frequency adaptation}{16}{}\protected@file@percent } \newlabel{eq:toy_env_pure}{{15}{17}{}{}{}} @@ -266,7 +267,7 @@ \newlabel{eq:toy_env_noise}{{18}{17}{}{}{}} \newlabel{eq:toy_log_noise}{{19}{17}{}{}{}} \newlabel{eq:toy_highpass_noise}{{20}{17}{}{}{}} -\@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. }}{19}{}\protected@file@percent } +\@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 measures 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. }}{19}{}\protected@file@percent } \newlabel{fig:log-hp}{{5}{19}{}{}{}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.1.3}Thresholding nonlinearity \& temporal averaging}{20}{}\protected@file@percent } \newlabel{eq:pdf_split}{{21}{20}{}{}{}} @@ -279,13 +280,13 @@ \newlabel{fig:thresh-lp_species}{{7}{25}{}{}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Intensity invariance along the full model pathway}{26}{}\protected@file@percent } \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.1}Including logarithmic compression}{26}{}\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}. }}{28}{}\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 measures 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 measures 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}. }}{28}{}\protected@file@percent } \newlabel{fig:pipeline_full}{{8}{28}{}{}{}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.2}Excluding logarithmic compression}{29}{}\protected@file@percent } -\@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}. }}{30}{}\protected@file@percent } +\@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 measures 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 measures 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}. }}{30}{}\protected@file@percent } \newlabel{fig:pipeline_short}{{9}{30}{}{}{}} \@writefile{toc}{\contentsline {subsubsection}{\numberline {3.3.3}Intensity invariance in a naturalistic setting}{31}{}\protected@file@percent } -\@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 $\mu _{f_i}$. }}{32}{}\protected@file@percent } +\@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 measures 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 measures 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 $\mu _{f_i}$. }}{32}{}\protected@file@percent } \newlabel{fig:pipeline_field}{{10}{32}{}{}{}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Interspecific and intraspecific feature variability}{33}{}\protected@file@percent } \@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. }}{34}{}\protected@file@percent } diff --git a/main.bcf-SAVE-ERROR b/main.bcf-SAVE-ERROR index f7fce77..a9ae7df 100644 --- a/main.bcf-SAVE-ERROR +++ b/main.bcf-SAVE-ERROR @@ -2412,25 +2412,3 @@ rokem2006spike clemens2011efficient clemens2012nonlinear - rehbein1974structure - rehbein1976auditory - eichendorf1980projections - gray1960fine - ronacher1986routes - bauer1987separate - bhavsar2017brain - clemens2011efficient - windmill2008time - malkin2014energy - machens2001discrimination - machens2001representation - suga1960peripheral - gollisch2002energy - romer1976informationsverarbeitung - gollisch2004input - fisch2012channel - hildebrandt2009origin - clemens2010intensity - ronacher1986routes - bauer1987separate - bhavsar2017brain diff --git a/main.blg b/main.blg index 1ee70be..5f773e0 100644 --- a/main.blg +++ b/main.blg @@ -1,71 +1,71 @@ [0] Config.pm:307> INFO - This is Biber 2.19 [0] Config.pm:310> INFO - Logfile is 'main.blg' -[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 +[36] biber:340> INFO - === Do Mai 14, 2026, 14:31:13 +[43] Biber.pm:419> INFO - Reading 'main.bcf' +[72] Biber.pm:979> INFO - Found 55 citekeys in bib section 0 +[78] Biber.pm:4419> INFO - Processing section 0 +[82] Biber.pm:4610> INFO - Looking for bibtex file 'cite.bib' for section 0 +[84] bibtex.pm:1713> INFO - LaTeX decoding ... +[114] bibtex.pm:1519> INFO - Found BibTeX data source 'cite.bib' +[292] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable' +[292] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized' +[292] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US' +[292] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US' +[316] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8' +[328] bbl.pm:763> INFO - Output to main.bbl +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 10, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 21, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 38, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 49, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 58, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 73, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 82, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 91, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 100, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 109, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 118, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 127, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 136, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 157, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 178, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 187, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 196, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 207, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 218, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 229, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 240, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 249, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 258, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 269, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 278, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 289, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 300, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 309, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 328, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 337, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 400, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 419, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 428, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 437, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 456, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 491, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 526, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 535, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 556, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 565, warning: 6 characters of junk seen at toplevel +[328] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 576, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 587, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 619, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 648, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 658, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 667, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 688, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 709, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 720, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 729, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 749, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 766, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 775, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 800, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_CAWg/347c261ec4135a5723bef5c751f5078f_89475.utf8, line 817, warning: 6 characters of junk seen at toplevel +[329] Biber.pm:133> INFO - 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[41 <./figures/fig_kernel_sd_perc_field_appendix.pdf>] [42 <./figures/fig_invariance_cross_species_thresh_appendix.pdf>] (./main.aux) *********** @@ -920,15 +920,15 @@ Package logreq Info: Writing requests to 'main.run.xml'. ) Here is how much of TeX's memory you used: - 20895 strings out of 474222 - 454186 string characters out of 5748732 + 20896 strings out of 474222 + 454210 string characters out of 5748732 1937975 words of memory out of 5000000 - 42882 multiletter control sequences out of 15000+600000 + 42883 multiletter control sequences out of 15000+600000 570077 words of font info for 81 fonts, out of 8000000 for 9000 1143 hyphenation exceptions out of 8191 - 94i,18n,93p,1747b,1738s stack positions out of 10000i,1000n,20000p,200000b,200000s + 94i,18n,93p,1749b,1738s stack positions out of 10000i,1000n,20000p,200000b,200000s -Output written on main.pdf (42 pages, 33580313 bytes). +Output written on main.pdf (42 pages, 33581775 bytes). PDF statistics: 2622 PDF objects out of 2984 (max. 8388607) 1165 compressed objects within 12 object streams diff --git a/main.pdf b/main.pdf index 32fca74..cf4ec50 100644 Binary files a/main.pdf and b/main.pdf differ diff --git a/main.synctex.gz b/main.synctex.gz index be94153..d32096c 100644 Binary files a/main.synctex.gz and b/main.synctex.gz differ diff --git a/main.tex b/main.tex index 87450ba..7774436 100644 --- a/main.tex +++ b/main.tex @@ -283,7 +283,7 @@ grasshopper auditory pathway, from the initial reception of sound waves up to the generation of a high-dimensional, time-varying feature representation that is suitable for species-specific song recognition. We provide a side-by-side account of the known physiological processing steps and their functional -approximation by basic mathematical operations. We then elaborate on two key +approximation by basic mathematical operations. We then elaborate on the key mechanisms that drive the emergence of intensity-invariant song representations within the auditory pathway. @@ -317,6 +317,14 @@ within the auditory pathway. % $\rightarrow$ Abstract, simplify, formalize $\rightarrow$ Functional model framework \section{Methods} +% This maybe does not quite fit here, but it is the most general part of the +% methods and applies throughout the whole section, so I put it here for now. +All modeling, data analysis, and data visualization was performed in +Python~3.12.3 except for the pathway overview~(Fig.\,\ref{fig:pathway}), which +was assembled in Inkscape~1.2. The code base for the model pathway is available +as the \textit{thunderhopper} package, version 1.0, on PyPi. Any audio data was +inspected and edited with the help of the \textit{audian} package, version 2.4, +on PyPi. \subsection{Functional model of the grasshopper song recognition pathway} @@ -617,7 +625,7 @@ mollis}~(Tab.\,\ref{tab:species_list}). \label{tab:species_list} \end{table} -\subsubsection{Generating synthetic input signals} +\subsubsection{Generation of 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, @@ -668,6 +676,7 @@ 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} +\label{sec:intensity_measures} All intensity measures were calculated over a manually labeled segment within each song. Segments always excluded the first and last few syllables to allow @@ -726,6 +735,26 @@ such, the ratio of intensity measures is referred to as SNR in the following. \subsection{Field data-based analysis of the model pathway} +Field recordings were taken on a meadow in the vicinity of the University of +Tübingen, Germany, during the day in August~2024. All recordings were taken +using a custom hand-held microphone array that was assembled from eight +omnidirectional AV-TEFE TCM141 condenser microphones. The microphones were +arranged in a linear configuration with a spacing of 30\,cm between adjacent +microphones and oriented in the same direction along the axis of the array. All +microphones were connected to a custom 8-channel amplificitation and +digitization system based on a Teensy 4.1 microcontroller with real-time clock +and microSD card storage. Recordings were written to the microSD card +in~\textit{.wav}~format with a sampling rate of 96\,kHz and an amplitude scale +in arbitrary units. The microphone array was held at a height of approximately +30\,cm above the ground, which was slightly above the height of most +surrounding vegetation and at the same height as the singing grasshopper. The +array was moved as close to the grasshopper as possible without interrupting +its song production, which amounts to an approximate offset distance of 10\,cm +between the animal and the leading microphone. Care was taken to maintain a +stable position and height of the microphone array during recording. The +resulting recordings were then processed through the model pathway and analysed +according to the procedure described in Section~\ref{sec:intensity_measures}. + \section{Results} \subsection{Mechanisms driving the emergence of intensity invariance} @@ -809,7 +838,7 @@ more robust input representation and higher input SNR. $\env(t)$ for different $\sca$. \textbf{a}:~Noiseless case. \textbf{b}:~Noisy case. - \textbf{Bottom}:~Intensity metrics over a range of $\sca$. + \textbf{Bottom}:~Intensity measures over a range of $\sca$. \textbf{c}:~Noiseless case: Standard deviations $\sigma_x$ of $\filt(t)$ and $\env(t)$. \textbf{d}:~Noisy case: Ratios of $\sigma_x$ of $\filt(t)$ and @@ -948,7 +977,7 @@ is a recurring phenomenon that is further addressed in the following sections. $\db(t)$, and $\adapt(t)$ for different $\sca$. \textbf{a}:~Noiseless case. \textbf{b}:~Noisy case. - \textbf{Bottom}:~Intensity metrics over a range of $\sca$. + \textbf{Bottom}:~Intensity measures over a range of $\sca$. \textbf{c}:~Noiseless case: Standard deviations $\sigma_x$ of $\env(t)$, $\db(t)$, and $\adapt(t)$. \textbf{d}:~Noisy case: Ratios of $\sigma_x$ of $\env(t)$, @@ -1225,15 +1254,15 @@ compression step~(Fig.\,\ref{fig:pipeline_short}). For this analysis, input $\raw(t)$ --- including both song component $\soc(t)$ and noise component $\noc(t)$ --- was rescaled and processed throughout all steps of the model pathway~(Fig.\,\ref{fig:pipeline_full}a) up to the feature -set $f_i(t)$. As before, the standard deviation was used as intensity metric +set $f_i(t)$. As before, the standard deviation was used as intensity measure for each resulting representation except $b_i(t)$ and $f_i(t)$. For $f_i(t)$, the average feature value $\muf$ was used, while $b_i(t)$ was omitted from the -analysis. Plotting each intensity metric over +analysis. Plotting each intensity measure over $\sca$~(Fig.\,\ref{fig:pipeline_full}b) reinforces many of the previous observations. For ease of visualization, the kernel-specific curves for $c_i(t)$ and $f_i(t)$ were summarized by their median. Representations prior to logarithmic compression --- $\filt(t)$ and $\env(t)$ --- show a linear increase -of the intensity metric for larger $\sca$ on a double-logarithmic scale. +of the intensity measure for larger $\sca$ on a double-logarithmic scale. Representations after logarithmic compression --- $\db(t)$, $\adapt(t)$, and $c_i(t)$ --- are the first to reach a saturation regime and do so at approximately the same $\sca$ because they are separated only by linear @@ -1243,7 +1272,7 @@ that of $c_i(t)$, which suggests that the second mechanism of thresholding and temporal averaging can indeed improve intensity invariance beyond the first mechanism of logarithmic compression and adaptation. The difference in saturation points is best illustrated based on the ratio of each intensity -metric to the respective pure-noise reference +measure to the respective pure-noise reference value~(Fig.\,\ref{fig:pipeline_full}d). However, compressing $f_i(t)$ into a median across $k_i(t)$ conceils many kernel-specific details. It is therefore necessary to consider the development of each $f_i(t)$ over $\sca$ @@ -1299,13 +1328,13 @@ in principle, work together towards an intensity-invariant song representation. \textbf{a}:~Example representations of $\filt(t)$, $\env(t)$, $\db(t)$, $\adapt(t)$, $c_i(t)$, and $f_i(t)$ for different $\sca$. - \textbf{b}:~Intensity metrics over $\sca$. For $c_i(t)$ + \textbf{b}:~Intensity measures over $\sca$. For $c_i(t)$ and $f_i(t)$, the median over kernels is shown. Dots indicate $95\,\%$ curve span for $\db(t)$, $\adapt(t)$, $c_i(t)$, and $f_i(t)$. \textbf{c}:~Average value $\mu_{f_i}$ of each feature $f_i(t)$ over $\sca$. - \textbf{d}:~Ratios of intensity metrics to the respective + \textbf{d}:~Ratios of intensity measures to the respective reference value for input $\raw(t)=\noc(t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. @@ -1329,7 +1358,7 @@ $\adapt(t)$ is merely a highpass filtered version of $\env(t)$; and $\db(t)$ is missing entirely~(Fig.\,\ref{fig:pipeline_short}a). As expected, all representations prior to the thresholding nonlinearity $\nl$ --- $\filt(t)$, $\env(t)$, $\adapt(t)$, and $c_i(t)$ --- show a linear increase of the -intensity metric for larger $\sca$, while $f_i(t)$ is the only representation +intensity measure for larger $\sca$, while $f_i(t)$ is the only representation to reach a saturation regime~(Fig.\,\ref{fig:pipeline_short}bd). The saturated $\muf$ are distributed over a much broader range of values than in the previous analysis~(Fig.\,\ref{fig:pipeline_short}c). Intriguingly, the @@ -1382,12 +1411,12 @@ guaranteed simply by disabling logarithmic compression. \textbf{a}:~Example representations of $\filt(t)$, $\env(t)$, $\adapt(t)$, $c_i(t)$, and $f_i(t)$ for different $\sca$. - \textbf{b}:~Intensity metrics over $\sca$. For $c_i(t)$ + \textbf{b}:~Intensity measures over $\sca$. 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 $\sca$. - \textbf{d}:~Ratios of intensity metrics to the respective + \textbf{d}:~Ratios of intensity measures to the respective reference value for input $\raw(t)=\noc(t)$. For $c_i(t)$ and $f_i(t)$, the median over kernel-specific ratios is shown. @@ -1416,14 +1445,14 @@ $d$ from the sender, ranging from $10\,$cm to $220\,$cm with intervals of $30\,$cm between microphones. The precise value of $\sca$ that corresponds to a given $d$ cannot be determined in a straightforward manner, but $\sca$ is expected to be inversely proportional to $d$ based on the inverse-square law of -sound propagation. All intensity metrics and ratios thereof were hence plotted +sound propagation. All intensity measures and ratios thereof were hence plotted over $1/d$ on a double-logarithmic scale, which is insofar comparable to previous analyses that a decade on the $1/d$ axis corresponds to a decade on the $\sca$ axis. To complicate matters further, the $1/d$ axis is sampled too sparsely to determine saturation points as before based on the $95\,\%$ curve span. Instead, one has to rely on the slope of the curve to assess if, and at which $1/d$, a given representation reaches a saturation regime. Bearing these -limitations in mind, the intensity metrics of each representation over +limitations in mind, the intensity measures of each representation over $1/d$~(Fig.\,\ref{fig:pipeline_field}b) follow a pattern that is consistent with the results of the previous simulation-based analysis~(Fig.\,\ref{fig:pipeline_full}b): The standard deviations of @@ -1439,7 +1468,7 @@ $d=10\,$cm corresponds to a value of $\sca$ between 10 and 20 based on comparison with the simulation-based analysis~(Fig.\,\ref{fig:pipeline_full}b). The saturated $\muf$ are distributed over a comparably narrow range of values, which could in parts be a property of the songs of \textit{P. parallelus}~(see -also Fig.\,\ref{fig:thresh-lp_species}bc). The ratios of each intensity metric +also Fig.\,\ref{fig:thresh-lp_species}bc). The ratios of each intensity measure to the respective pure-noise reference value are not aligned across representations~(Fig.\,\ref{fig:pipeline_field}d) or kernels~(Fig.\,\ref{fig:pipeline_field}ef) but serve to consolidate the @@ -1468,11 +1497,11 @@ distances~(Fig.\,\ref{fig:pipeline_field}a, bottom row). \textbf{a}:~$\filt(t)$, $\env(t)$, $\db(t)$, $\adapt(t)$, $c_i(t)$, 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 $c_i(t)$ + \textbf{b}:~Intensity measures 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 + \textbf{d}:~Ratios of intensity measures 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 diff --git a/python/save_inv_data_short.py b/python/save_inv_data_short.py index d393790..f605535 100644 --- a/python/save_inv_data_short.py +++ b/python/save_inv_data_short.py @@ -27,7 +27,7 @@ example_file = { 'Omocestus_rufipes': 'Omocestus_rufipes_DJN_32-40s724ms-48s779ms', 'Pseudochorthippus_parallelus': 'Pseudochorthippus_parallelus_GBC_88-6s678ms-9s32.3ms' }[target_species] -data_paths = search_files(target_species, dir='../data/processed/') +data_paths = search_files(target_species, incl=['DJN', '611ms'], dir='../data/processed/') noise_path = '../data/processed/white_noise_sd-1.npz' thresh_path = '../data/inv/short/thresholds.npz' pre_stages = ['filt', 'env'] @@ -42,8 +42,10 @@ thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3]) # SUBSET SETTINGS: kernels = None -types = None -sigmas = None +types = np.array([1, -1, 2, -2, 3, -3, 4, -4]) +# types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5, 6, -6, 7, -7, 8, -8, 9, -9, 10, -10] +sigmas = np.array([0.001, 0.002, 0.004, 0.008, 0.016]) +# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032] # PREPARATION: pure_noise = np.load(noise_path)['raw'] @@ -54,8 +56,6 @@ thresh_abs = thresh_rel[:, None] * thresh_data['sds'][None, :] for data_path, name in zip(data_paths, crop_paths(data_paths)): save_detailed = example_file in name print(f'Processing {name}') - if "DJN" in name: - continue # Get song recording (prior to anything): data, config = load_data(data_path, files='raw')