Wrote results rect-lp and log-hp :)
Finished some more figure captions.
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\@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 }
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\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). }{}{}}
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\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 $x_{\text {adapt}}(t)$. }{}{}}
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\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{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 }
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\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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\@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. }}{14}{}\protected@file@percent }
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\newlabel{fig:log-hp}{{5}{14}{\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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\@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}, once for the noisy case (solid lines) and once for the noiseless case (dotted lines). Dots indicate $95\,\%$ curve span (noisy case). \textbf {e}:~$\mu _f$ over a range of $\alpha $. \textbf {f}:~$\mu _f$ over the standard deviation of noisy input $x_{\text {adapt}}$ corresponding to the values of $\alpha $ shown in \textbf {e}. }}{16}{}\protected@file@percent }
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\newlabel{fig:thresh-lp_single}{{6}{16}{\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}, once for the noisy case (solid lines) and once for the noiseless case (dotted lines). Dots indicate $95\,\%$ curve span (noisy case). \textbf {e}:~$\mu _f$ over a range of $\alpha $. \textbf {f}:~$\mu _f$ over the standard deviation of noisy input $x_{\text {adapt}}$ corresponding to the values of $\alpha $ shown in \textbf {e}. }{}{}}
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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 }}{17}{}\protected@file@percent }
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\newlabel{fig:thresh-lp_species}{{7}{17}{\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 }{}{}}
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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. }}{18}{}\protected@file@percent }
|
||||
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|
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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}. }}{20}{}\protected@file@percent }
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|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 576, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 587, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 619, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 648, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 658, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 667, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 688, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 709, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 720, warning: 6 characters of junk seen at toplevel
|
||||
[1620] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 729, warning: 6 characters of junk seen at toplevel
|
||||
[1621] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 749, warning: 6 characters of junk seen at toplevel
|
||||
[1621] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 766, warning: 6 characters of junk seen at toplevel
|
||||
[1621] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 775, warning: 6 characters of junk seen at toplevel
|
||||
[1621] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 800, warning: 6 characters of junk seen at toplevel
|
||||
[1621] Biber.pm:131> WARN - BibTeX subsystem: C:\Users\jonah\AppData\Local\Temp\biber_tmp_n7RG\347c261ec4135a5723bef5c751f5078f_20112.utf8, line 817, warning: 6 characters of junk seen at toplevel
|
||||
[1621] Biber.pm:133> INFO - WARNINGS: 55
|
||||
[37] biber:340> INFO - === Mo Mai 4, 2026, 19:48:34
|
||||
[45] Biber.pm:419> INFO - Reading 'main.bcf'
|
||||
[75] 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'
|
||||
[296] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'variable = shifted' with 'variable = non-ignorable'
|
||||
[296] UCollate.pm:68> INFO - Overriding locale 'en-US' defaults 'normalization = NFD' with 'normalization = prenormalized'
|
||||
[296] Biber.pm:4239> INFO - Sorting list 'nyt/global//global/global' of type 'entry' with template 'nyt' and locale 'en-US'
|
||||
[296] Biber.pm:4245> INFO - No sort tailoring available for locale 'en-US'
|
||||
[320] bbl.pm:660> INFO - Writing 'main.bbl' with encoding 'UTF-8'
|
||||
[331] bbl.pm:763> INFO - Output to main.bbl
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 10, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 21, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 38, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 49, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 58, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 73, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 82, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 91, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 100, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 109, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 118, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 127, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 136, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 157, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 178, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 187, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 196, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 207, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 218, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 229, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 240, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 249, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 258, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 269, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 278, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 289, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 300, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 309, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 328, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 337, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 400, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 419, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 428, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 437, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 456, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 491, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 526, warning: 6 characters of junk seen at toplevel
|
||||
[331] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 535, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 556, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 565, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 576, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 587, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 619, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 648, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 658, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 667, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 688, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 709, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 720, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 729, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 749, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 766, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 775, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 800, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:131> WARN - BibTeX subsystem: /tmp/biber_tmp_vMHw/347c261ec4135a5723bef5c751f5078f_55959.utf8, line 817, warning: 6 characters of junk seen at toplevel
|
||||
[332] Biber.pm:133> INFO - WARNINGS: 55
|
||||
|
||||
348
main.fdb_latexmk
348
main.fdb_latexmk
@@ -1,180 +1,184 @@
|
||||
# Fdb version 4
|
||||
["biber main"] 1777829759 "main.bcf" "main.bbl" "main" 1777829958 0
|
||||
"./cite.bib" 1771664051 28308 4290db0c91f7b5055e25472ef913f6b4 ""
|
||||
"main.bcf" 1777829957 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
|
||||
["biber main"] 1777916914.1072 "main.bcf" "main.bbl" "main" 1777916953.0495 0
|
||||
"cite.bib" 1770904753.08918 27483 4290db0c91f7b5055e25472ef913f6b4 ""
|
||||
"main.bcf" 1777916952.98174 112931 2a478116d80ebb1ada7083a24facd6e3 "pdflatex"
|
||||
(generated)
|
||||
"main.bbl"
|
||||
"main.blg"
|
||||
(rewritten before read)
|
||||
["pdflatex"] 1777829955 "c:/Users/jonah/Desktop/phd/paper/paper_2025/main.tex" "main.pdf" "main" 1777829958 0
|
||||
"C:/Users/jonah/AppData/Local/MiKTeX/fonts/map/pdftex/pdftex.map" 1770058784 279778 aa8c8b08a6e6f5b912fcdb1120e47ad8 ""
|
||||
"C:/Users/jonah/AppData/Local/MiKTeX/miktex/data/le/pdftex/pdflatex.fmt" 1692800637 24120620 9856d41a93bb8d2530a663260e901d35 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/amsfonts/cmextra/cmex7.tfm" 1233951848 1004 54797486969f23fa377b128694d548df ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/amsfonts/cmextra/cmex8.tfm" 1233951848 988 bdf658c3bfc2d96d3c8b02cfc1c94c20 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/amsfonts/symbols/msam10.tfm" 1233951854 916 f87d7c45f9c908e672703b83b72241a3 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/amsfonts/symbols/msam7.tfm" 1233951854 928 2dc8d444221b7a635bb58038579b861a ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/amsfonts/symbols/msbm10.tfm" 1233951854 908 2921f8a10601f252058503cc6570e581 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/amsfonts/symbols/msbm7.tfm" 1233951854 940 228d6584342e91276bf566bcf9716b83 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmbx10.tfm" 1136765053 1328 c834bbb027764024c09d3d2bf908b5f0 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmbx12.tfm" 1136765053 1324 c910af8c371558dc20f2d7822f66fe64 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmbxti10.tfm" 1136765053 1532 9162035f4e7176612125649e348e2195 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmex10.tfm" 1136765053 992 662f679a0b3d2d53c1b94050fdaa3f50 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmmi10.tfm" 1136765053 1528 abec98dbc43e172678c11b3b9031252a ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmmi12.tfm" 1136765053 1524 4414a8315f39513458b80dfc63bff03a ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmmi6.tfm" 1136765053 1512 f21f83efb36853c0b70002322c1ab3ad ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmmi8.tfm" 1136765053 1520 eccf95517727cb11801f4f1aee3a21b4 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmr10.tfm" 1136765053 1296 45809c5a464d5f32c8f98ba97c1bb47f ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmr12.tfm" 1136765053 1288 655e228510b4c2a1abe905c368440826 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmr17.tfm" 1136765053 1292 296a67155bdbfc32aa9c636f21e91433 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmr6.tfm" 1136765053 1300 b62933e007d01cfd073f79b963c01526 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmr8.tfm" 1136765053 1292 21c1c5bfeaebccffdb478fd231a0997d ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmsy10.tfm" 1136765053 1124 6c73e740cf17375f03eec0ee63599741 ""
|
||||
"C:/Users/jonah/AppData/Local/Programs/MiKTeX/fonts/tfm/public/cm/cmsy6.tfm" 1136765053 1116 933a60c408fc0a863a92debe84b2d294 ""
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336
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336
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@@ -33,7 +33,7 @@
|
||||
%\bibstyle
|
||||
%\citation
|
||||
|
||||
\title{Emergent intensity invariance in a physiologically inspired model of the grasshopper auditory system}
|
||||
\title{Emergent intensity invariance vs. signal-to-noise ratio at three consecutive processing stages along the grasshopper song recognition pathway}
|
||||
\author{Jona Hartling, Jan Benda}
|
||||
\date{}
|
||||
|
||||
@@ -403,7 +403,7 @@ pathway, logarithmic compression is achieved by conversion to decibel scale
|
||||
\db(t)\,=\,20\,\cdot\,\dec \frac{\env(t)}{\dbref}, \qquad \dbref\,=\,1
|
||||
\label{eq:log}
|
||||
\end{equation}
|
||||
relative to the maximum intensity $\dbref$ of the signal envelope $\env(t)$.
|
||||
relative to the common reference intensity $\dbref$.
|
||||
Both the receptor neurons~(\bcite{romer1976informationsverarbeitung};
|
||||
\bcite{gollisch2004input}; \bcite{fisch2012channel}) and, on a larger scale,
|
||||
the subsequent local interneurons~(\bcite{hildebrandt2009origin};
|
||||
@@ -555,7 +555,7 @@ can be read out by a simple linear classifier.
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\section{Two mechanisms driving the emergence of intensity-invariant song representation}
|
||||
\section{Mechanisms driving the emergence of\\intensity-invariant song representation}
|
||||
|
||||
% Still missing the SNR analysis. Should be able to write around it for now.
|
||||
The robustness of song recognition is tied to the degree of intensity
|
||||
@@ -573,6 +573,54 @@ specific operations involved, as outlined in the following sections.
|
||||
|
||||
\subsection{Full-wave rectification \& lowpass filtering}
|
||||
|
||||
The first nonlinear transformation along the model pathway is the full-wave
|
||||
rectification of the tympanal signal $\filt(t)$ during the extraction of the
|
||||
signal envelope (Eq.\,\ref{eq:env}). Rectification transforms the distribution
|
||||
of $\filt(t)$ from an approximately zero-centered distribution with both
|
||||
positive and negative values into a strictly non-negative distribution. Signal
|
||||
envelope $\env(t)$ is then obtained by lowpass filtering the rectified
|
||||
$\filt(t)$. The effects of this transformation pair on SNR and potential
|
||||
intensity invariance were analyzed by rescaling and processing the input signal
|
||||
$\raw(t)$ and comparing standard deviations between the resulting $\filt(t)$
|
||||
and $\env(t)$, once for the noiseless case~(Fig.\,\ref{fig:rect-lp}a) and once
|
||||
for the noisy case~(Fig.\,\ref{fig:rect-lp}b). In addition, the cutoff
|
||||
frequency $\fc$ of the lowpass filter was varied to investigate the influence
|
||||
of different filter bandwidths. In the noiseless case, the standard deviations
|
||||
of $\filt(t)$ and $\env(t)$ are each reduced compared to the input $\raw(t)$ by
|
||||
a multiplicative factor. These factors are constant across all $\sca$, which
|
||||
results in a downward shift of the respective curve on a double-logarithmic
|
||||
scale, away from the diagonal~(Fig.\,\ref{fig:rect-lp}c). For $\filt(t)$, the
|
||||
reduction is a consequence of the bandpass filtering~(Eq.\,\ref{eq:bandpass})
|
||||
of $\raw(t)$. For $\env(t)$, the standard deviation is further reduced compared
|
||||
to $\filt(t)$. Rectification contributes much less to this reduction than
|
||||
lowpass filtering. The degree of reduction by lowpass filtering depends on the
|
||||
cutoff frequency $\fc$, with lower $\fc$ (narrow bandwidth) resulting in a
|
||||
stronger reduction. In the noisy case, the standard deviations of $\filt(t)$
|
||||
and $\env(t)$ can be related to the respective pure-noise reference standard
|
||||
deviation~(Fig.\,\ref{fig:rect-lp}d). This causes each curve to start with a
|
||||
constant regime of SNR values near 1 for smaller $\sca$, which reflects the
|
||||
dominance of the noise component $\noc(t)$ over the song component $\soc(t)$ in
|
||||
the input $\raw(t)$. For larger $\sca$, all curves transition into a regime of
|
||||
linearly increasing SNR on a double-logarithmic scale. For $\filt(t)$, the
|
||||
linear part of the curve deviates only slightly from the diagonal. For
|
||||
$\env(t)$, however, the transition occurs at lower $\sca$ compared to
|
||||
$\filt(t)$, and the linear part of the curve is shifted leftward away from the
|
||||
diagonal, which means that higher SNR values are achieved for the same $\sca$.
|
||||
This effect is more pronounced for lower $\fc$ of the lowpass filter and is
|
||||
presumably caused by the attenuation of high-frequency components in the
|
||||
signal, which are more prominent in the noise component $\noc(t)$ than in the
|
||||
song component $\soc(t)$. The effect also appears relatively consistent across
|
||||
different species, although small variations based on different song structures
|
||||
and distributions exist~(Fig.\,\ref{fig:rect-lp}e). In summary, the standard
|
||||
deviation of $\env(t)$ has never been observed to transition into a saturation
|
||||
regime for larger $\sca$ but rather continues to increase proportionally to
|
||||
$\sca$ for all tested $\fc$, in both the noiseless and the noisy case and
|
||||
across different species. Consequently, the combination of rectification and
|
||||
lowpass filtering does not contribute to intensity invariance. However, this
|
||||
transformation pair does improve the SNR of $\env(t)$ relative to $\filt(t)$
|
||||
and thus provides subsequent processing stages with a more robust input
|
||||
representation and higher input SNR.
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_rect_lp.pdf}
|
||||
@@ -605,73 +653,113 @@ specific operations involved, as outlined in the following sections.
|
||||
|
||||
\subsection{Logarithmic compression \& spike-frequency adaptation}
|
||||
|
||||
The first notable emergence of intensity invariance along the model pathway
|
||||
occurs during the transformation of the signal envelope $\env(t)$ into the
|
||||
logarithmically scaled envelope $\db(t)$ and then into the intensity-adapted
|
||||
envelope $\adapt(t)$. In order to disentangle the interplay of logarithmic
|
||||
compression and adaptation, $\env(t)$ can be rewritten as a synthetic mixture
|
||||
The second nonlinear transformation along the model pathway is the logarithmic
|
||||
compression of the signal envelope $\env(t)$ into $\db(t)$, Eq.\,\ref{eq:log},
|
||||
which is then followed by the highpass filtering of $\db(t)$,
|
||||
Eq.\,\ref{eq:highpass}, to obtain the intensity-adapted envelope $\adapt(t)$.
|
||||
The interplay of this transformation pair was analyzed by rescaling and
|
||||
processing the input signal $\filt(t)$ and comparing standard deviations
|
||||
between the resulting $\env(t)$, $\db(t)$, and $\adapt(t)$. It is necessary to
|
||||
use $\filt(t)$ as input for this analysis instead of $\env(t)$, because
|
||||
$\env(t)$ results from a nonlinear transformation and hence cannot be
|
||||
synthesized as an additive mixture of song component $\soc(t)$ and noise
|
||||
component $\noc(t)$. % <-- Sentence may be methods section material.
|
||||
However, it is much easier to conceive a mathematical description of the
|
||||
effects of logarithmic compression and adaptation if $\env(t)$ itself is
|
||||
assumed to be composed of $\soc(t)$ and $\noc(t)$. In the noiseless
|
||||
case~(Fig.\,\ref{fig:log-hp}a), $\env(t)$ takes the form of
|
||||
\begin{equation}
|
||||
\env(t)\,=\,\sca\,\cdot\,\soc(t), \qquad \env(t)\,>\,0\enspace\forall\enspace t\,\in\,\mathbb{R}
|
||||
\label{eq:toy_env_pure}
|
||||
\end{equation}
|
||||
The standard deviation of $\env(t)$ increases linearly with $\sca$ on a
|
||||
double-logarithmic scale and is slightly reduced~(Fig.\,\ref{fig:log-hp}c)
|
||||
compared to the input $\filt(t)$, which is consistent with the results of the
|
||||
previous analysis~(Fig.\,\ref{fig:rect-lp}c). By conversion of $\env(t)$ to
|
||||
decibel scale, $\sca$ turns from a multiplicative scale in linear space into an
|
||||
additive term, or offset, in logarithmic space:
|
||||
\begin{equation}
|
||||
\db(t)\,=\,20\,\cdot\,\dec \left[\,\sca\,\cdot\,s(t)\,\right]\,=\,20\,\cdot\,\left[\dec \sca\,+\,\dec s(t)\right], \qquad \sca\,>\,0
|
||||
\label{eq:toy_log_pure}
|
||||
\end{equation}
|
||||
The highpass filtering of $\db(t)$ can be approximated as a subtraction of the
|
||||
local signal offset within a suitable time interval $0 \ll \thp <
|
||||
\frac{1}{\fc}$:
|
||||
\begin{equation}
|
||||
\begin{split}
|
||||
\adapt(t)\,\approx\,\db(t)\,-\,20\,\cdot\,\dec \sca\,=\,20\,\cdot\,\dec s(t)
|
||||
\end{split}
|
||||
\label{eq:toy_highpass_pure}
|
||||
\end{equation}
|
||||
This eliminates $\sca$ from $\adapt(t)$ and thus renders it perfectly
|
||||
intensity-invariant, with a constant standard deviation of around 10\,dB across
|
||||
all $\sca>0$~(Fig.\,\ref{fig:log-hp}c). In contrast, in the noisy
|
||||
case~(Fig.\,\ref{fig:log-hp}b), $\env(t)$ takes the form of
|
||||
\begin{equation}
|
||||
\env(t)\,=\,\sca\,\cdot\,\soc(t)\,+\,\noc(t), \qquad \env(t)\,>\,0\enspace\forall\enspace t\,\in\,\mathbb{R}
|
||||
\label{eq:toy_env}
|
||||
\label{eq:toy_env_noise}
|
||||
\end{equation}
|
||||
of a song component $\soc(t)$ with variable multiplicative scale $\sca\geq0$
|
||||
and a fixed-scale noise component $\noc(t)$. Both $\soc(t)$ and $\noc(t)$ are
|
||||
assumed to have unit variance. By conversion of $\env(t)$ to decibel
|
||||
scale~(Eq.\,\ref{eq:log}), $\sca$ turns from a multiplicative scale in linear
|
||||
space into an additive term, or offset, in logarithmic space
|
||||
Similar to the previous analysis~(Fig.\,\ref{fig:rect-lp}d), the ratio of the
|
||||
standard deviation of $\env(t)$ to its pure-noise reference standard deviation
|
||||
on a double-logarithmic scale follows a constant regime for small $\sca$ and a
|
||||
linearly increasing regime for larger $\sca$~(Fig.\,\ref{fig:log-hp}d). Decibel
|
||||
conversion of $\env(t)$
|
||||
% \begin{equation}
|
||||
% \begin{split}
|
||||
% \db(t)\,&=\,\dec \frac{\alpha\,\cdot\,s(t)\,+\,\eta(t)}{\dbref}\\
|
||||
% &=\,\dec \frac{\alpha}{\dbref}\,+\,\dec \left[s(t)\,+\,\frac{\eta(t)}{\alpha}\right], \qquad \sca\,>\,0
|
||||
% \db(t)\,&=\,20\,\cdot\,\dec \left[\,\sca\,\cdot\,s(t)\,+\,\eta(t)\,\right]\\
|
||||
% &=\,20\,\cdot\,\left(\dec \sca\,+\,\dec \left[s(t)\,+\,\frac{\eta(t)}{\sca}\right]\right), \qquad \sca\,>\,0
|
||||
% \end{split}
|
||||
% \label{eq:toy_log}
|
||||
% \label{eq:toy_log_noise}
|
||||
% \end{equation}
|
||||
\begin{equation}
|
||||
\begin{split}
|
||||
\db(t)\,&=\,20\,\cdot\,\dec \left[\,\sca\,\cdot\,s(t)\,+\,\eta(t)\,\right]\\
|
||||
&=\,20\,\cdot\,\left(\dec \sca\,+\,\dec \left[s(t)\,+\,\frac{\eta(t)}{\sca}\right]\right), \qquad \sca\,>\,0
|
||||
\end{split}
|
||||
\label{eq:toy_log}
|
||||
\db(t)\,=\,20\,\cdot\,\left(\dec \sca\,+\,\dec \left[s(t)\,+\,\frac{\eta(t)}{\sca}\right]\right), \qquad \sca\,>\,0
|
||||
\label{eq:toy_log_noise}
|
||||
\end{equation}
|
||||
allows for the separation of $\sca$ from $\soc(t)$ but introduces a scaling of
|
||||
$\noc(t)$ by the inverse of $\sca$, which remains present even after the offset
|
||||
subtraction:
|
||||
\begin{equation}
|
||||
\begin{split}
|
||||
\adapt(t)\,\approx\,20\,\cdot\,\dec\left[s(t)\,+\,\frac{\eta(t)}{\sca}\right]
|
||||
\end{split}
|
||||
\label{eq:toy_highpass_noise}
|
||||
\end{equation}
|
||||
which allows for its separation from $\soc(t)$ but introduces a scaling of
|
||||
$\noc(t)$ by the inverse of $\sca$. The subsequent
|
||||
highpass filtering~(Eq.\,\ref{eq:highpass}) of $\db(t)$ can then be
|
||||
approximated as a subtraction of the local offset within a suitable time
|
||||
interval $0 \ll \thp < \frac{1}{\fc}$:
|
||||
% \begin{equation}
|
||||
% \begin{split}
|
||||
% \adapt(t)\,\approx\,\db(t)\,-\,\dec \frac{\sca}{\dbref}\,=\,\dec\left[s(t)\,+\,\frac{\eta(t)}{\sca}\right], \qquad \sca\,>\,0
|
||||
% \adapt(t)\,\approx\,\db(t)\,-\,20\,\cdot\,\dec \sca\,=\,20\,\cdot\,\dec\left[s(t)\,+\,\frac{\eta(t)}{\sca}\right]
|
||||
% \end{split}
|
||||
% \label{eq:toy_highpass}
|
||||
% \label{eq:toy_highpass_noise}
|
||||
% \end{equation}
|
||||
\begin{equation}
|
||||
\begin{split}
|
||||
\adapt(t)\,\approx\,\db(t)\,-\,20\,\cdot\,\dec \sca\,=\,20\,\cdot\,\dec\left[s(t)\,+\,\frac{\eta(t)}{\sca}\right], \qquad \sca\,>\,0
|
||||
\end{split}
|
||||
\label{eq:toy_highpass}
|
||||
\end{equation}
|
||||
This means that $\sca$ cannot be entirely eliminated from $\adapt(t)$, only
|
||||
redistributed between $\soc(t)$ and $\noc(t)$. In consequence, if $\sca$ is
|
||||
sufficiently large ($\sca\gg1$), $\noc(t)$ is attenuated to the point of being
|
||||
negligible, so that $\adapt(t)$ represents $\soc(t)$ in a scale-free manner. If
|
||||
$\soc(t)$ and $\noc(t)$ are at similar scales ($\sca\approx1$), $\adapt(t)$
|
||||
largely resembles $\db(t)$. However, if $\sca$ is sufficiently small
|
||||
($\sca\ll1$), $\noc(t)$ masks $\soc(t)$ even after the intensity adaptation.
|
||||
Therefore, the effective intensity invariance of $\adapt(t)$ relative to
|
||||
$\env(t)$ is limited by the initial scaling of $\soc(t)$ relative to $\noc(t)$;
|
||||
that is, the signal-to-noise ratio (SNR) of $\env(t)$ with ($\sca>0$) and
|
||||
without ($\sca=0$) song component $\soc(t)$
|
||||
\begin{equation}
|
||||
\text{SNR}(\sca)\,=\,\frac{\xvar}{\nvar}\,=\,\frac{\alpha^{2}\,\cdot\,\svar\,+\,\nvar}{\nvar}\,=\,\alpha^{2}\,+\,1, \qquad \svar\,=\,\nvar\,=\,1
|
||||
\label{eq:toy_snr}
|
||||
\end{equation}
|
||||
which depends quadratically on $\sca$ if $\soc(t)\perp\noc(t)$. Overall, the
|
||||
combination of logarithmic compression and adaptation allows for the
|
||||
equalization of different sufficiently large song scales, which is essential
|
||||
for intensity-invariant song representation. However, this mechanism is unable
|
||||
to recover songs that have already sunken below the noise floor, which
|
||||
emphasizes the importance of a sufficiently high SNR at the intial reception of
|
||||
the signal for reliable song recognition.
|
||||
This means that, in the noisy case, $\sca$ cannot be entirely eliminated from
|
||||
$\adapt(t)$, only redistributed between $\soc(t)$ and $\noc(t)$. If $\sca$ is
|
||||
sufficiently large ($\sca\gg1$, saturation regime), $\noc(t)$ is attenuated to
|
||||
the point of being negligible, so that $\adapt(t)$ is a scale-free
|
||||
representation of $\soc(t)$. If $\sca$ and $\noc(t)$ are at similar scales
|
||||
($\sca\approx1$, transient regime), $\adapt(t)$ largely resembles $\db(t)$.
|
||||
Finally, if $\sca$ is sufficiently small ($0<\sca\ll1$, noise regime),
|
||||
$\noc(t)$ masks $\soc(t)$ even after the intensity adaptation. Accordingly, the
|
||||
effective intensity invariance of $\adapt(t)$ through logarithmic compression
|
||||
and adaptation is limited by the SNR of $\env(t)$: Songs that have already
|
||||
sunken into the noise floor at the level of $\env(t)$ cannot be recovered by
|
||||
subsequent processing steps, which emphasizes the importance of the SNR
|
||||
improvement by rectification and lowpass filtering during the previous
|
||||
processing step~(Fig.\,\ref{fig:rect-lp}d). The general pattern of noise
|
||||
regime, transient regime, and saturation regime remains consistent across
|
||||
different species~(Fig.\,\ref{fig:log-hp}e). However, the specific value of
|
||||
$\sca$ at which the saturation regime is reached (see appendix
|
||||
Fig.\,\ref{fig:app_log-hp_saturation}) and the maximum SNR value of $\adapt(t)$
|
||||
within the saturation regime vary considerably between and within species. For
|
||||
example, \textit{C. biguttulus} and \textit{C. mollis} display a noticably
|
||||
lower maximum SNR of $\adapt(t)$ compared to other species. These differences
|
||||
are not to be underestimated, since the SNR of $\adapt(t)$ within the
|
||||
saturation regime determines the maximum input SNR for subsequent processing
|
||||
steps. In other words, the fact that $\adapt(t)$ eventually reaches a
|
||||
saturation regime is, of course, desirable in the context of intensity
|
||||
invariance, but it also means to pass up on the higher SNR values that are
|
||||
achieved by $\env(t)$ for the same $\sca$ (up to several orders of magnitude,
|
||||
Fig.\,\ref{fig:log-hp}d). This trade-off between intensity invariance and SNR
|
||||
--- and the consequences it has further downstream along the pathway --- are
|
||||
adressed in the following sections.
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
@@ -744,30 +832,6 @@ the signal for reliable song recognition.
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
|
||||
% \caption{\textbf{Rectification and lowpass filtering improves SNR
|
||||
% but does not contribute to intensity invariance.}
|
||||
% Input $\raw(t)$ consists of song component $\soc(t)$ scaled by
|
||||
% $\sca$ with optional noise component $\noc(t)$ and is
|
||||
% successively transformed into tympanal signal $\filt(t)$ and
|
||||
% envelope $\env(t)$. Different line styles indicate different
|
||||
% cutoff frequencies $\fc$ of the lowpass filter extracting
|
||||
% $\env(t)$.
|
||||
% \textbf{Top}:~Example representations of $\filt(t)$ and
|
||||
% $\env(t)$ for different $\sca$.
|
||||
% \textbf{a}:~Noiseless case.
|
||||
% \textbf{b}:~Noisy case.
|
||||
% \textbf{Bottom}:~Intensity metrics over a range of $\sca$.
|
||||
% \textbf{c}:~Noiseless case: Standard deviations of $\filt(t)$
|
||||
% and $\env(t)$.
|
||||
% \textbf{d}:~Noisy case: Ratios of standard deviations of
|
||||
% $\filt(t)$ and $\env(t)$ to the respective reference standard
|
||||
% deviation for input $\raw(t)=\noc(t)$.
|
||||
% \textbf{e}:~Ratios of standard deviations of $\env(t)$ as in
|
||||
% \textbf{b} for different species (averaged over songs and
|
||||
% recordings, see appendix Fig.\,\ref{fig:app_rect-lp}).
|
||||
% }
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_thresh_lp_species.pdf}
|
||||
@@ -796,17 +860,67 @@ the signal for reliable song recognition.
|
||||
curve span of the norm across all three $\mu_{f_i}$ per
|
||||
species.
|
||||
\textbf{d}:~Noiseless case.
|
||||
\textbf{e}:~Noisy case. Shaded areas
|
||||
\textbf{e}:~Noisy case. Shaded areas indicate the average
|
||||
minimum $\mu_{f_i}$ across all species-specific trajectories.
|
||||
}
|
||||
\label{fig:thresh-lp_species}
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
% \caption{\textbf{Rectification and lowpass filtering improves SNR
|
||||
% but does not contribute to intensity invariance.}
|
||||
% Input $\raw(t)$ consists of song component $\soc(t)$ scaled by
|
||||
% $\sca$ with optional noise component $\noc(t)$ and is
|
||||
% successively transformed into tympanal signal $\filt(t)$ and
|
||||
% envelope $\env(t)$. Different line styles indicate different
|
||||
% cutoff frequencies $\fc$ of the lowpass filter extracting
|
||||
% $\env(t)$.
|
||||
% \textbf{Top}:~Example representations of $\filt(t)$ and
|
||||
% $\env(t)$ for different $\sca$.
|
||||
% \textbf{a}:~Noiseless case.
|
||||
% \textbf{b}:~Noisy case.
|
||||
% \textbf{Bottom}:~Intensity metrics over a range of $\sca$.
|
||||
% \textbf{c}:~Noiseless case: Standard deviations of $\filt(t)$
|
||||
% and $\env(t)$.
|
||||
% \textbf{d}:~Noisy case: Ratios of standard deviations of
|
||||
% $\filt(t)$ and $\env(t)$ to the respective reference standard
|
||||
% deviation for input $\raw(t)=\noc(t)$.
|
||||
% \textbf{e}:~Ratios of standard deviations of $\env(t)$ as in
|
||||
% \textbf{b} for different species (averaged over songs and
|
||||
% recordings, see appendix Fig.\,\ref{fig:app_rect-lp}).
|
||||
% }
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_full_Omocestus_rufipes.pdf}
|
||||
\caption{\textbf{Step-wise emergence of intensity invariant song
|
||||
representation along the model pathway.}
|
||||
\caption{\textbf{Step-wise emergence of intensity-invariant song
|
||||
representation along the full model pathway.}
|
||||
Input $\raw(t)$ consists of song component $\soc(t)$
|
||||
scaled by $\sca$ with added noise component $\noc(t)$ and
|
||||
is processed up to the feature set $f_i(t)$. Different
|
||||
color shades indicate different types of Gabor kernels
|
||||
with specific lobe number $\kn$ and either $+$ or $-$
|
||||
sign, sorted (dark to light) first by increasing $\kn$ and
|
||||
then by sign~($1\,\leq\,\kn\,\leq\,4$; first $+$, then $-$
|
||||
for each $\kn$; five kernel widths $\kw$ of 1, 2, 4, 8,
|
||||
and $16\,$ms per type; 8 types, 40 kernels in total).
|
||||
\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 $c_i(t)$
|
||||
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
|
||||
reference value for input $\raw(t)=\noc(t)$. 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 $\sca$ that
|
||||
correspond to $95\,\%$ curve span for $c_i(t)$ and
|
||||
$f_i(t)$. Dots indicate the values from \textbf{b}.
|
||||
}
|
||||
\label{fig:pipeline_full}
|
||||
\end{figure}
|
||||
@@ -816,7 +930,34 @@ the signal for reliable song recognition.
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_short_Omocestus_rufipes.pdf}
|
||||
\caption{\textbf{Step-wise emergence of intensity invariant song
|
||||
representation along the model pathway.}
|
||||
representation along the model pathway without logarithmic
|
||||
compression.}
|
||||
Input $\raw(t)$ consists of song component $\soc(t)$
|
||||
scaled by $\sca$ with added noise component $\noc(t)$ and
|
||||
is processed up to the feature set $f_i(t)$, skipping
|
||||
$\db(t)$. Different color shades indicate different types
|
||||
of Gabor kernels with specific lobe number $\kn$ and
|
||||
either $+$ or $-$ sign, sorted (dark to light) first by
|
||||
increasing $\kn$ and then by
|
||||
sign~($1\,\leq\,\kn\,\leq\,4$; first $+$, then $-$ for
|
||||
each $\kn$; five kernel widths $\kw$ of 1, 2, 4, 8, and
|
||||
$16\,$ms per type; 8 types, 40 kernels in total).
|
||||
\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 $c_i(t)$
|
||||
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
|
||||
reference value for input $\raw(t)=\noc(t)$. 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 $\sca$ that
|
||||
correspond to $95\,\%$ curve span for $f_i(t)$. Dots
|
||||
indicate the value from \textbf{b}.
|
||||
}
|
||||
\label{fig:pipeline_short}
|
||||
\end{figure}
|
||||
@@ -825,7 +966,28 @@ the signal for reliable song recognition.
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_features_cross_species.pdf}
|
||||
\caption{\textbf{Inter- and intraspecific feature variability.}
|
||||
\caption{\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 $\raw(t)$. 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 $\kn$ and either $+$ or $-$ sign,
|
||||
sorted (dark to light) first by increasing $\kn$ and then
|
||||
by sign~($1\,\leq\,\kn\,\leq\,4$; first $+$, then $-$ for
|
||||
each $\kn$; five kernel widths $\kw$ 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 left}:~Distribution of correlation
|
||||
coefficients $\rho$ for each interspecific and
|
||||
intraspecific comparison. Dots indicate single $\rho$
|
||||
values.
|
||||
}
|
||||
\label{fig:feat_cross_species}
|
||||
\end{figure}
|
||||
|
||||
@@ -164,7 +164,7 @@ ylabels = dict(
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
feat='$f_i$',
|
||||
raw=['$m$', '$\\mu_{f_i}$'],
|
||||
base=['$m\\,/\\,m_{\\eta}$', '$\\sigma_{c_i}$', '$\\mu_{f_i}$', '$\\text{PDF}_{\\alpha}$']
|
||||
base=['$m\\,/\\,m_{\\eta}$', '$\\sigma_{c_i}\\,/\\,\\sigma_{\\eta_i}$', '$\\mu_{f_i}\\,/\\,\\mu_{\\eta_i}$', '$\\text{PDF}_{\\alpha}$']
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
|
||||
@@ -57,7 +57,7 @@ snip_cutoff = np.array([np.nan, 2500, 250, 25])[2]
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 32/2.54),
|
||||
figsize=(32/2.54, 20/2.54),
|
||||
)
|
||||
super_grid_kwargs = dict(
|
||||
nrows=3,
|
||||
@@ -68,7 +68,7 @@ super_grid_kwargs = dict(
|
||||
right=1,
|
||||
bottom=0,
|
||||
top=1,
|
||||
height_ratios=[1, 1, 1]
|
||||
height_ratios=[1, 1, 2]
|
||||
)
|
||||
subfig_specs = dict(
|
||||
pure=(0, slice(None)),
|
||||
|
||||
@@ -162,7 +162,7 @@ ylabels = dict(
|
||||
conv='$c_i$\n$[\\text{dB}]$',
|
||||
feat='$f_i$',
|
||||
raw=['$m$', '$\\mu_{f_i}$'],
|
||||
base=['$m\\,/\\,m_{\\eta}$', '$\\mu_{f_i}$', '$\\text{PDF}_{\\alpha}$']
|
||||
base=['$m\\,/\\,m_{\\eta}$', '$\\mu_{f_i}\\,/\\,\\mu_{\\eta_i}$', '$\\text{PDF}_{\\alpha}$']
|
||||
)
|
||||
xlab_big_kwargs = dict(
|
||||
y=0,
|
||||
|
||||
@@ -1,3 +1,5 @@
|
||||
import gc
|
||||
import copy
|
||||
import numpy as np
|
||||
from thunderhopper.modeltools import load_data, save_data
|
||||
from thunderhopper.filetools import search_files, crop_paths
|
||||
@@ -16,7 +18,7 @@ target_species = [
|
||||
# 'Gomphocerippus_rufus',
|
||||
# 'Omocestus_rufipes',
|
||||
# 'Pseudochorthippus_parallelus',
|
||||
][1]
|
||||
][0]
|
||||
example_file = {
|
||||
'Chorthippus_biguttulus': 'Chorthippus_biguttulus_GBC_94-17s73.1ms-19s977ms',
|
||||
'Chorthippus_mollis': 'Chorthippus_mollis_DJN_41_T28C-46s4.58ms-1m15s697ms',
|
||||
@@ -110,8 +112,8 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
scaled = song * scale + noise
|
||||
|
||||
# Process mixture (excluding features):
|
||||
signals, rates = process_signal(config, returns=pre_stages,
|
||||
signal=scaled, rate=rate)
|
||||
signals, _ = process_signal(config, returns=pre_stages,
|
||||
signal=scaled, rate=rate)
|
||||
# Store non-feature results:
|
||||
for stage in pre_stages:
|
||||
# Log intensity measures:
|
||||
@@ -120,12 +122,16 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
# Log optional snippet data:
|
||||
if save_detailed and scale in example_scales:
|
||||
scale_ind = np.nonzero(example_scales == scale)[0][0]
|
||||
snippets[f'snip_{stage}'][:, ..., scale_ind] = signals[stage]
|
||||
snippets[f'snip_{stage}'][:, ..., scale_ind] = copy.deepcopy(signals[stage])
|
||||
|
||||
conv = copy.deepcopy(signals['conv'])
|
||||
del scaled, signals
|
||||
gc.collect()
|
||||
|
||||
# Execute piecewise again:
|
||||
for j, thresholds in enumerate(thresh_abs):
|
||||
# Finalize processing:
|
||||
feat = sosfilter((signals['conv'] > thresholds).astype(float),
|
||||
feat = sosfilter((conv > thresholds).astype(float),
|
||||
rate, config['feat_fcut'], 'lp',
|
||||
padtype='fixed', padlen=config['padlen'])
|
||||
|
||||
@@ -134,19 +140,27 @@ for data_path, name in zip(data_paths, crop_paths(data_paths)):
|
||||
|
||||
# Log optional snippet data:
|
||||
if save_detailed and scale in example_scales:
|
||||
snippets['snip_feat'][:, :, scale_ind, j] = feat
|
||||
snippets['snip_feat'][:, :, scale_ind, j] = copy.deepcopy(feat)
|
||||
|
||||
del feat
|
||||
gc.collect()
|
||||
|
||||
# Save analysis results:
|
||||
if save_path is not None:
|
||||
data = dict(
|
||||
archive = dict(
|
||||
scales=scales,
|
||||
example_scales=example_scales,
|
||||
thresh_rel=thresh_rel,
|
||||
thresh_abs=thresh_abs,
|
||||
)
|
||||
data.update(measures)
|
||||
archive.update(measures)
|
||||
if save_detailed:
|
||||
data.update(snippets)
|
||||
save_data(save_path + name, data, config, overwrite=True)
|
||||
archive.update(snippets)
|
||||
save_data(save_path + name, archive, config, overwrite=True)
|
||||
del archive
|
||||
del measures, data, config, conv
|
||||
if save_detailed:
|
||||
del snippets
|
||||
gc.collect()
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
Reference in New Issue
Block a user