Captioned appendix figures.
Polished some figures. Shortened existing figure captions.
This commit is contained in:
j-hartling
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main.tex
444
main.tex
@@ -228,6 +228,7 @@ intensity-invariant song representations, the interaction between these
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mechanisms, the overall capacity for intensity invariance in the system, and
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the ethological implications of our findings.
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\newpage
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\section{Methods}
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% This maybe does not quite fit here, but it is the most general part of the
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% methods and applies throughout the whole section, so I put it here for now.
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@@ -289,8 +290,8 @@ the following sections.
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representations~(boxes) and transformations~(arrows) along
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the model pathway. All representations are time-varying.
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1st half: Preprocessing stage~(one-dimensional
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representation). 2nd half: Feature extraction
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stage~(high-dimensional representation). }
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representations). 2nd half: Feature extraction
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stage~(high-dimensional representations). }
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\label{fig:pathway}
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\end{figure}
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@@ -347,8 +348,8 @@ the following feature extraction stage.
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\begin{figure}[!ht]
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\centering
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\includegraphics[width=\textwidth]{figures/fig_pre_stages.pdf}
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\caption{\textbf{Representations of a song of \textit{O. rufipes} during
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the preprocessing stage.}
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\caption{\textbf{Song representations during the preprocessing stage.}
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Example song of \textit{O. rufipes}.
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\textbf{a}:~Bandpass filtered tympanal signal $\filt(t)$.
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\textbf{b}:~Signal envelope $\env(t)$.
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\textbf{c}:~Logarithmically compressed envelope $\db(t)$.
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@@ -483,14 +484,15 @@ or a simple linear classifier.
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\begin{figure}[!ht]
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\centering
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\includegraphics[width=\textwidth]{figures/fig_feat_stages.pdf}
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\caption{\textbf{Representations of a song of \textit{O. rufipes} during
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the feature extraction stage.}
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\caption{\textbf{Song representations during the feature extraction stage.}
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Example song of \textit{O. rufipes}.
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Different color shades indicate different types of Gabor
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kernels with specific lobe number $\kn$ and either $+$ or
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$-$ sign, sorted (dark to light) first by increasing $\kn$
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and then by sign~($1\,\leq\,\kn\,\leq\,4$; first $+$, then
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$-$ for each $\kn$; two kernel widths $\kw$ of $4\,$ms and
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$32\,$ms per type; 8 types, 16 kernels in total).
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kernels with specific lobe number $\kni$ and either $+$ or
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$-$ sign, sorted (dark to light) first by increasing
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$\kni$ and then by sign~($1\,\leq\,\kni\,\leq\,4$; first
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$+$, then $-$ for each $\kni$; two kernel widths $\kwi$ of
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$4\,$ms and $32\,$ms per type; 8 types, 16 kernels in
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total).
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\textbf{a}:~Kernel-specific filter responses $c_i(t)$.
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\textbf{b}:~Binary responses $b_i(t)$.
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\textbf{c}:~Finalized features $f_i(t)$.}
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@@ -653,12 +655,13 @@ which is a reasonable assumption for the raw $\soc(t)$ and $\noc(t)$. However,
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the dependency of the ratio on $\sca$ is not necessarily the same for
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representations that are transformed from $x(t)$ by nonlinear operations, since
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these change the relationship of $\soc(t)$ and $\noc(t)$ in an unpredictable
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fashion. Furthermore, the ratio is not a proper SNR of the representation
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because it does not relate $\soc(t)$ to $\noc(t)$ within the representation but
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rather the entire representation to $\noc(t)$ alone. However, it still provides
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a useful measure of the relative intensity of a representation with and without
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$\soc(t)$, which is the closest we can get to the SNR of the representation. As
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such, the ratio of intensity measures is referred to as SNR in the following.
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fashion~(see appendix Fig.\,\ref{fig:app_env-sd}). Furthermore, the ratio is
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not a proper SNR of the representation because it does not relate $\soc(t)$ to
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$\noc(t)$ within the representation but rather the entire representation to
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$\noc(t)$ alone. However, it still provides a useful measure of the relative
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intensity of a representation with and without $\soc(t)$, which is the closest
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we can get to the SNR of the representation. As such, the ratio of intensity
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measures is referred to as SNR in the following.
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% Is this legal? "SNR" is much shorter than "ratio of intensity measure to the pure-noise reference measure".
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% Haven't used it much yet, sticked to "ratio" in most cases.
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@@ -694,9 +697,10 @@ $\thr$ has been specified as a multiple of the pure-noise reference standard
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deviation $\sigma_{c_i}$ for input $x(t)=\noc(t)$. This ensures that $\thr$ as
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well as the resulting $b_i(t)$ and $f_i(t)$ are comparable across different
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$k_i(t)$ because each pure-noise $c_i(t)$ approximately follows a normal
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distribution~(see appendix
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distribution around zero~(see appendix
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Figs.\,\ref{fig:app_thresh-lp_kern-sd}-\ref{fig:app_field_kern-sd}).
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\newpage
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\section{Results}
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\subsection{Mechanisms driving the emergence of intensity invariance}
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@@ -767,25 +771,23 @@ more robust input representation and higher input SNR.
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\includegraphics[width=\textwidth]{figures/fig_invariance_rect_lp.pdf}
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\caption{\textbf{Rectification and lowpass filtering improves SNR
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but does not contribute to intensity invariance.}
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Input $\raw(t)$ consists of song component $\soc(t)$ scaled by
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$\sca$ with optional noise component $\noc(t)$ and is
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successively transformed into tympanal signal $\filt(t)$ and
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envelope $\env(t)$. Different line styles indicate different
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cutoff frequencies $\fc$ of the lowpass filter extracting
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$\env(t)$.
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\textbf{Top}:~Example representations of $\filt(t)$ and
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$\env(t)$ for different $\sca$.
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Input $\raw(t)$ consists of $\soc(t)$ scaled by $\sca$ with
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optional $\noc(t)$ and is successively transformed into
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tympanal signal $\filt(t)$ and envelope $\env(t)$.
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\textbf{Top}:~Examples of $\filt(t)$ and $\env(t)$ for
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different $\sca$.
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\textbf{a}:~Noiseless case.
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\textbf{b}:~Noisy case.
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\textbf{Bottom}:~Intensity measures over a range of $\sca$.
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\textbf{c}:~Noiseless case: Standard deviations $\sigma_x$ of
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$\filt(t)$ and $\env(t)$.
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\textbf{d}:~Noisy case: Ratios of $\sigma_x$ of $\filt(t)$ and
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$\env(t)$ to the respective reference standard deviation
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$\sigma_{\eta}$ for input $\raw(t)=\noc(t)$.
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\textbf{e}:~Ratios of $\sigma_x$ to $\sigma_{\eta}$ of
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\textbf{Bottom}:~Intensity measures over $\sca$. Different
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line styles indicate different cutoff frequencies $\fc$ of the
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lowpass filter extracting $\env(t)$.
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\textbf{c}:~Noiseless case: Standard deviation $\sigma_x$ of
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$\filt(t)$ and $\env(t)$, respectively.
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\textbf{d}:~Noisy case: Ratio of $\sigma_x$ to the respective
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pure-noise reference $\sigma_{\eta}$ for $\sca=0$.
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\textbf{e}:~Ratio of $\sigma_x$ to $\sigma_{\eta}$ of
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$\env(t)$ as in \textbf{d} for different species (averaged
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over songs and recordings, see appendix
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over songs and recordings, appendix
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Fig.\,\ref{fig:app_rect-lp}).
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}
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\label{fig:rect-lp}
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@@ -907,28 +909,26 @@ is a recurring phenomenon that is further addressed in the following sections.
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\caption{\textbf{Intensity invariance through logarithmic compression and
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adaptation is restricted by the noise floor and decreases
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SNR.}
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Input $\filt(t)$ consists of song component $\soc(t)$
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scaled by $\sca$ with optional noise component $\noc(t)$
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Input $\filt(t)$ consists of $\soc(t)$
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scaled by $\sca$ with optional $\noc(t)$
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and is successively transformed into envelope $\env(t)$,
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logarithmically compressed envelope $\db(t)$, and
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intensity-adapted envelope $\adapt(t)$.
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\textbf{Top}:~Example representations of $\env(t)$,
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$\db(t)$, and $\adapt(t)$ for different $\sca$.
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\textbf{Top}:~Examples of $\env(t)$, $\db(t)$, and
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$\adapt(t)$ for different $\sca$.
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\textbf{a}:~Noiseless case.
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\textbf{b}:~Noisy case.
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\textbf{Bottom}:~Intensity measures over a range of $\sca$.
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\textbf{c}:~Noiseless case: Standard deviations $\sigma_x$
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of $\env(t)$, $\db(t)$, and $\adapt(t)$.
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\textbf{d}:~Noisy case: Ratios of $\sigma_x$ of $\env(t)$,
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$\db(t)$, and $\adapt(t)$ to the respective reference
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standard deviation $\sigma_{\eta}$ for input
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$\filt(t)=\noc(t)$. Shaded areas indicate $5\,\%$ (dark
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grey) and $95\,\%$ (light grey) curve span for
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$\adapt(t)$.
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\textbf{e}:~Ratios of $\sigma_x$ to $\sigma_{\eta}$ of
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\textbf{Bottom}:~Intensity measures over $\sca$.
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\textbf{c}:~Noiseless case: Standard deviation $\sigma_x$
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of $\env(t)$, $\db(t)$, and $\adapt(t)$, respectively.
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\textbf{d}:~Noisy case: Ratio of $\sigma_x$ to the
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respective pure-noise reference $\sigma_{\eta}$ for
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$\sca=0$. Shaded areas indicate $5\,\%$ (dark grey) and
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$95\,\%$ (light grey) curve span for $\adapt(t)$.
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\textbf{e}:~Ratio of $\sigma_x$ to $\sigma_{\eta}$ of
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$\adapt(t)$ as in \textbf{d} for different species
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(averaged over songs and recordings, see appendix
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Fig\,\ref{fig:app_log-hp_curves}). Dots indicate $95\,\%$
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(averaged over songs and recordings, appendix
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Fig.\,\ref{fig:app_log-hp_curves}). Dots indicate $95\,\%$
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curve span per species.
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}
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\label{fig:log-hp}
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@@ -1043,33 +1043,32 @@ intensity invariance are further explored in a later section.
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\caption{\textbf{Intensity invariance through thresholding and temporal
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averaging is mediated by the interaction of threshold
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value and noise floor.}
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Input $\adapt(t)$ consists of song component $\soc(t)$
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scaled by $\sca$ with optional noise component $\noc(t)$
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and is transformed into single kernel response $c(t)$,
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binary response $b(t)$, and feature $f(t)$. Different
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color shades indicate different threshold values $\Theta$
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(multiples of reference standard deviation $\sigma_{\eta}$
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of $c(t)$ for input $\adapt(t)=\noc(t)$, with darker
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colors for higher $\Theta$).
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\textbf{Left}:~Noisy case: Example representations of
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$\adapt(t)$ as well as $c(t)$, $b(t)$, and $f(t)$ for
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different $\sca$.
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Input $\adapt(t)$ consists $\soc(t)$ scaled by $\sca$ with
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optional $\noc(t)$ and is transformed into single kernel
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response $c(t)$, binary response $b(t)$, and feature
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$f(t)$. Different color shades indicate different
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threshold values $\Theta$ (multiples of pure-noise
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standard deviation $\sigma_{\eta}$ of $c(t)$ for $\sca=0$,
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with darker colors for higher $\Theta$. See also appendix
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Fig.\,\ref{fig:app_thresh-lp_kern-sd}).
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\textbf{Left}:~Noisy case: Examples of $\adapt(t)$ as well
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as $c(t)$, $b(t)$, and $f(t)$ for different $\sca$.
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\textbf{a}:~$\adapt(t)$ with kernel $k(t)$ in black.
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\textbf{b\,-\,d}: $c(t)$, $b(t)$, and $f(t)$ based on the
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same $\adapt(t)$ from \textbf{a} but with different
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same $\adapt(t)$ from \textbf{a} but for different
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$\Theta$.
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\textbf{Right}:~Average value $\mu_f$ of $f(t)$ for each
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$\Theta$ from \textbf{b\,-\,d}. Dots indicate $95\,\%$
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curve span (noisy case).
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\textbf{e}:~$\mu_f$ over a range of $\sca$, once for the
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noisy case (solid lines) and once for the noiseless case
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(dotted lines).
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\textbf{f}:~Noisy case: $\mu_f$ over the standard
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deviation of input $\adapt$ corresponding to the values of
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$\sca$ shown in \textbf{e}. Shaded area indicates standard
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deviations that would be capped in the output $\adapt(t)$
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of the previous transformation pair (see
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Fig.\,\ref{fig:log-hp}cd).
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\textbf{e}:~$\mu_f$ over $\sca$, once for the noisy case
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(solid lines) and once for the noiseless case (dotted
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lines).
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\textbf{f}:~Noisy case: $\mu_f$ over standard deviation
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$\sigma_{\text{adapt}}$ of input $\adapt$ corresponding to
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$\sca$ shown in \textbf{e}. Shaded area indicates values
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of $\sigma_{\text{adapt}}$ that are capped in the output
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$\adapt(t)$ of the previous transformation pair
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(Fig.\,\ref{fig:log-hp}cd).
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}
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\label{fig:thresh-lp_single}
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\end{figure}
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@@ -1148,29 +1147,29 @@ point of $f_i(t)$ less relevant.
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saturates at different points in feature space.}
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Same input and processing as in
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Fig.\,\ref{fig:thresh-lp_single} but with three different
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kernels $k_i$, each with a single kernel-specific
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threshold value $\thr=0.5\cdot\sigma_{\eta_i}$.
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kernels $k_i$ and a single kernel-specific threshold value
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$\thr=0.5\cdot\sigma_{\eta_i}$ (appendix
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Fig.\,\ref{fig:app_thresh-lp_kern-sd}).
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\textbf{a}:~Examples of species-specific grasshopper
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songs.
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\textbf{Middle}:~Average value $\mu_{f_i}$ of each feature
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\textbf{Middle}:~Average value $\muf$ of each feature
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$f_i(t)$ over $\sca$ per species (averaged over songs and
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recordings, see appendix
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Figs.\,\ref{fig:app_thresh-lp_pure} and
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\ref{fig:app_thresh-lp_noise}). Different color shades
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indicate different kernels $k_i$. Dots indicate $95\,\%$
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curve span per $k_i$.
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recordings, appendix Figs.\,\ref{fig:app_thresh-lp_pure}
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and \ref{fig:app_thresh-lp_noise}). Different color shades
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indicate different $k_i$. Dots indicate $95\,\%$ curve
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span per $k_i$.
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\textbf{b}:~Noiseless case.
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\textbf{c}:~Noisy case.
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\textbf{Bottom}:~2D feature spaces spanned by each pair of
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$f_i(t)$. Each trajectory corresponds to a
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species-specific combination of $\mu_{f_i}$ that develops
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species-specific combination of $\muf$ that develops
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with $\sca$ (colorbars). Horizontal dashes in the colorbar
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indicate $5\,\%$ (dark grey) and $95\,\%$ (light grey)
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curve span of the norm across all three $\mu_{f_i}$ per
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curve span of the norm across all three $\muf$ per
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species.
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\textbf{d}:~Noiseless case.
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\textbf{e}:~Noisy case. Shaded areas indicate the average
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minimum $\mu_{f_i}$ across all species-specific trajectories.
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minimum $\muf$ across all species-specific trajectories.
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}
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\label{fig:thresh-lp_species}
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\end{figure}
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@@ -1255,34 +1254,34 @@ in principle, work together towards an intensity-invariant song representation.
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\includegraphics[width=\textwidth]{figures/fig_invariance_full_Omocestus_rufipes.pdf}
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\caption{\textbf{Step-wise emergence of intensity-invariant song
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representations along the model pathway.}
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Input $\raw(t)$ consists of song component $\soc(t)$
|
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scaled by $\sca$ with added noise component $\noc(t)$ and
|
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is processed up to the feature set $f_i(t)$. Different
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color shades indicate different types of Gabor kernels
|
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with specific lobe number $\kn$ and either $+$ or $-$
|
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sign, sorted (dark to light) first by increasing $\kn$ and
|
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then by sign~($1\,\leq\,\kn\,\leq\,4$; first $+$, then $-$
|
||||
for each $\kn$; five kernel widths $\kw$ of 1, 2, 4, 8,
|
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and $16\,$ms per type; 8 types, 40 kernels in total).
|
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\textbf{a}:~Example representations of $\filt(t)$,
|
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$\env(t)$, $\db(t)$, $\adapt(t)$, $c_i(t)$, and $f_i(t)$
|
||||
for different $\sca$.
|
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\textbf{b}:~Intensity measures over $\sca$. For $c_i(t)$
|
||||
and $f_i(t)$, the median over kernels is shown. Dots
|
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Input $\raw(t)$ consists of $\soc(t)$ scaled by $\sca$
|
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with added $\noc(t)$ and is processed up to the feature
|
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set $f_i(t)$ using kernel-specific threshold values
|
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$\thr=2\cdot\sigma_{\eta_i}$ (appendix
|
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Fig.\,\ref{fig:app_full_kern-sd}). Different color shades
|
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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 $k_i(t)$ in total).
|
||||
\textbf{a}:~Examples of $\filt(t)$, $\env(t)$, $\db(t)$,
|
||||
$\adapt(t)$, $c_i(t)$, and $f_i(t)$ for different $\sca$.
|
||||
\textbf{b}:~Intensity measures over $\sca$. The median
|
||||
over $k_i(t)$ is shown for $c_i(t)$ and $f_i(t)$. 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 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.
|
||||
\textbf{e}:~Ratios of standard deviation $\sigma_{c_i}$ of
|
||||
\textbf{c}:~Average value $\muf$ of each $f_i(t)$
|
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over $\sca$.
|
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\textbf{d}:~Ratio of intensity measures from \textbf{b} to
|
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the respective pure-noise reference for $\sca=0$.
|
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\textbf{e}:~Ratio of standard deviation $\sigma_{c_i}$ of
|
||||
each $c_i(t)$.
|
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\textbf{f}:~Ratios of $\mu_{f_i}$.
|
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\textbf{f}:~Ratio of $\muf$.
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\textbf{g}:~Distributions of kernel-specific $\sca$ that
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correspond to $95\,\%$ curve span for $c_i(t)$ and
|
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$f_i(t)$. Dots indicate the values from \textbf{b}.
|
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$f_i(t)$. Dots indicate values based on the median from
|
||||
\textbf{b}.
|
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}
|
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\label{fig:pipeline_full}
|
||||
\end{figure}
|
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@@ -1337,32 +1336,24 @@ guaranteed simply by disabling logarithmic compression.
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_short_Omocestus_rufipes.pdf}
|
||||
\caption{\textbf{Effects of disabling logarithmic compression on intensity
|
||||
invariance along the 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 $f_i(t)$, 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 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 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.
|
||||
\textbf{e}:~Ratios of $\mu_{f_i}$.
|
||||
Same input and processing as in
|
||||
Fig.\,\ref{fig:pipeline_full}, using kernel-specific
|
||||
threshold values $\thr=2\cdot\sigma_{\eta_i}$ (appendix
|
||||
Fig.\,\ref{fig:app_short_kern-sd}), except that
|
||||
logarithmic compression and hence $\db(t)$ are skipped.
|
||||
\textbf{a}:~Examples of $\filt(t)$, $\env(t)$,
|
||||
$\adapt(t)$, $c_i(t)$, and $f_i(t)$ for different $\sca$.
|
||||
\textbf{b}:~Intensity measures over $\sca$. The median
|
||||
over $k_i(t)$ is shown for $c_i(t)$ and $f_i(t)$. Dot
|
||||
indicates $95\,\%$ curve span for $f_i(t)$.
|
||||
\textbf{c}:~Average value $\muf$ of each $f_i(t)$
|
||||
over $\sca$.
|
||||
\textbf{d}:~Ratio of intensity measures from \textbf{b} to
|
||||
the respective pure-noise reference for $\sca=0$.
|
||||
\textbf{e}:~Ratio of $\muf$.
|
||||
\textbf{f}:~Distribution of kernel-specific $\sca$ that
|
||||
correspond to $95\,\%$ curve span for $f_i(t)$. Dots
|
||||
indicate the value from \textbf{b}.
|
||||
correspond to $95\,\%$ curve span for $f_i(t)$. Dot
|
||||
indicates value based on the median from \textbf{b}.
|
||||
}
|
||||
\label{fig:pipeline_short}
|
||||
\end{figure}
|
||||
@@ -1426,26 +1417,27 @@ distances~(Fig.\,\ref{fig:pipeline_field}a, bottom row).
|
||||
Input $\raw(t)$ 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 $\kn$ and
|
||||
either $+$ or $-$ sign, sorted (dark to light) first by
|
||||
increasing $\kn$ and then by
|
||||
$f_i(t)$ using kernel-specific threshold values
|
||||
$\thr=2\cdot\sigma_{\eta_i}$ (appendix
|
||||
Fig.\,\ref{fig:app_field_kern-sd}). 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).
|
||||
$16\,$ms per type; 8 types, 40 $k_i(t)$ in total).
|
||||
\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 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
|
||||
\textbf{b}:~Intensity measures over $d$. The median over
|
||||
$k_i(t)$ is shown for $c_i(t)$ and $f_i(t)$.
|
||||
\textbf{c}:~Average value $\muf$ of each $f_i(t)$ over
|
||||
$d$.
|
||||
\textbf{d}:~Ratio of intensity measures from \textbf{b} to
|
||||
the respective value obtained from the noise reference.
|
||||
\textbf{e}:~Ratio of standard deviation $\sigma_{c_i}$ of
|
||||
each $c_i(t)$.
|
||||
\textbf{f}:~Ratios of $\mu_{f_i}$.
|
||||
\textbf{f}:~Ratios of $\muf$.
|
||||
}
|
||||
\label{fig:pipeline_field}
|
||||
\end{figure}
|
||||
@@ -1470,8 +1462,8 @@ song, so that each dot within a subplot corresponds to a single feature
|
||||
$f_i(t)$. For the intraspecific
|
||||
comparisons~(Fig.\,\ref{fig:feat_cross_species}, upper triangular), the pairs
|
||||
of $\muf$ are distributed closely around the diagonal, with a minimum
|
||||
correlation coefficient of $\rho=0.85$, a maximum of $\rho=0.99$, and a median
|
||||
of $\rho=0.92$. A given $f_i(t)$ thus tends to have a similar $\muf$ across
|
||||
correlation coefficient of $\rho=0.82$, a maximum of $\rho=0.99$, and a median
|
||||
of $\rho=0.91$. A given $f_i(t)$ thus tends to have a similar $\muf$ across
|
||||
different songs of the same species. In contrast, the pairs of $\muf$ for the
|
||||
interspecific comparisons~(Fig.\,\ref{fig:feat_cross_species}, lower
|
||||
triangular) are distributed in a variety of different ways, most in broader
|
||||
@@ -1479,7 +1471,7 @@ clouds (e.g. \textit{C. biguttulus} vs. \textit{C. mollis}) but some more
|
||||
narrowly around the diagonal (e.g. \textit{P. parallelus} vs. \textit{C.
|
||||
dispar}). The correlation coefficients $\rho$ vary widely between different
|
||||
interspecific comparisons, with a minimum of $\rho=-0.1$, a maximum of
|
||||
$\rho=0.92$, and a median of $\rho=0.53$. A given $f_i(t)$ therefore tends to
|
||||
$\rho=0.91$, and a median of $\rho=0.40$. A given $f_i(t)$ therefore tends to
|
||||
have a less similar $\muf$ across different species than within the same
|
||||
species, although certain exeptions exist~(Fig.\,\ref{fig:feat_cross_species},
|
||||
lower right). Accordingly, the feature representation that is generated by the
|
||||
@@ -1498,18 +1490,17 @@ natural song variation.
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_features_cross_species.pdf}
|
||||
\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
|
||||
Average value $\muf$ of each feature $f_i(t)$ against its
|
||||
counterpart from a 2nd feature set based on a different
|
||||
input $\raw(t)$. Data is based on the saturated $\muf$
|
||||
from Fig.\,\ref{fig:pipeline_full}. Each dot within a
|
||||
subplot represents a single $f_i(t)$. 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}.
|
||||
$16\,$ms per type; 8 types, 40 kernels in total).
|
||||
\textbf{Lower triangular}:~Interspecific comparisons
|
||||
between single songs of different species.
|
||||
\textbf{Upper triangular}:~Intraspecific comparisons
|
||||
@@ -1524,7 +1515,8 @@ natural song variation.
|
||||
\end{figure}
|
||||
\FloatBarrier
|
||||
|
||||
\section{Conclusions \& outlook}
|
||||
\newpage
|
||||
\section{Discussion}
|
||||
|
||||
% RIPPED FROM INTRODUCTION:
|
||||
|
||||
@@ -1677,105 +1669,189 @@ $\rightarrow$ Graded again but highly decorrelated from the acoustic stimulus\\
|
||||
$\rightarrow$ Parameters of a behavioral response may be graded (e.g. approach speed),
|
||||
initiation of one behavior over another is categorical (e.g. approach/stay)
|
||||
|
||||
\newpage
|
||||
\section{Appendix}
|
||||
|
||||
% Not sure if we really need this one. Might raise more questions than it
|
||||
% provides answers. The noise component is not stable throughout nonlinear
|
||||
% transformations, that is all the reader needs to know, i believe.
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_noise_env_sd_conversion_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\caption{\textbf{Conversion of the noise component by envelope extraction.}
|
||||
Standard deviation $\sigma_{\eta}$ of noise component
|
||||
$\noc(t)$ within the signal envelope $\env(t)$ over scale
|
||||
$\sca$. Based on input $\raw(t)$ with $\sigma_{\eta}=1$
|
||||
(corresponding to the analysis underlying
|
||||
Fig.\,\ref{fig:rect-lp}), using 100 realizations of
|
||||
$\noc(t)$.}
|
||||
\label{fig:app_env-sd}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_rect-lp_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\caption{\textbf{Species-specific data underlying Fig.\,\ref{fig:rect-lp}e.}
|
||||
Ratio of the standard deviation $\sigma_{\text{env}}$ to
|
||||
the pure-noise reference $\sigma_{\eta}$ of the signal
|
||||
envelope $\env(t)$ over scale $\sca$ for different cutoff
|
||||
frequencies $\fc$ of the lowpass filter extracting
|
||||
$\env(t)$. Solid lines and shaded areas indicate mean
|
||||
$\pm$ standard deviation across songs per recording.
|
||||
Dashed lines indicate mean across recordings (shown in
|
||||
Fig.\,\ref{fig:rect-lp}e).}
|
||||
\label{fig:app_rect-lp}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_log-hp_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\caption{\textbf{Species-specific data underlying Fig.\,\ref{fig:log-hp}e.}
|
||||
Ratio of the standard deviation $\sigma_{\text{adapt}}$ to
|
||||
the pure-noise reference $\sigma_{\eta}$ of the
|
||||
intensity-adapted envelope $\adapt(t)$ over scale $\sca$.
|
||||
Solid lines and shaded areas indicate mean $\pm$ standard
|
||||
deviation across songs per recording. Dashed lines
|
||||
indicate mean across recordings (shown in
|
||||
Fig.\,\ref{fig:log-hp}e).}
|
||||
\label{fig:app_log-hp_curves}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_saturation_log-hp_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\caption{\textbf{Species-specific saturation points underlying
|
||||
Fig.\,\ref{fig:log-hp}e.}
|
||||
Distribution of saturation points ($95\,\%$ curve span) of
|
||||
ratio $\sigma_{\text{adapt}} / \sigma_{\eta}$ of the
|
||||
intensity-adapted envelope $\adapt(t)$ over scale $\sca$
|
||||
across all available songs. Dots indicate the saturation
|
||||
point of the mean curve across songs and recordings (shown
|
||||
in Fig.\,\ref{fig:log-hp}e, see also appendix
|
||||
Fig.\,\ref{fig:app_log-hp_curves}).}
|
||||
\label{fig:app_log-hp_saturation}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_thresh-lp_pure_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\caption{\textbf{Species-specific data underlying Fig.\,\ref{fig:thresh-lp_species}bd.}
|
||||
Average value $\muf$ of each of the three features
|
||||
$f_i(t)$ over scale $\sca$ in the noiseless case. Solid
|
||||
lines and shaded areas indicate mean $\pm$ standard
|
||||
deviation across songs per recording. Dashed lines
|
||||
indicate mean across recordings (shown in
|
||||
Fig.\,\ref{fig:thresh-lp_species}bd).}
|
||||
\label{fig:app_thresh-lp_pure}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_thresh-lp_noise_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
}
|
||||
\caption{\textbf{Species-specific data underlying Fig.\,\ref{fig:thresh-lp_species}ce.}
|
||||
Average value $\muf$ of each of the three features
|
||||
$f_i(t)$ over scale $\sca$ in the noisy case. Solid lines
|
||||
and shaded areas indicate mean $\pm$ standard deviation
|
||||
across songs per recording. Dashed lines indicate mean
|
||||
across recordings (shown in
|
||||
Fig.\,\ref{fig:thresh-lp_species}ce).}
|
||||
\label{fig:app_thresh-lp_noise}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_thresh_lp_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
\caption{\textbf{Relation between threshold value and pure-noise feature
|
||||
value for Fig.\,\ref{fig:thresh-lp_single} and
|
||||
Fig.\,\ref{fig:thresh-lp_species}.}
|
||||
Proportion of pure-noise kernel response $c_i(t)$ that
|
||||
exceeds threshold value $\thr$ --- which determines the
|
||||
average value $\muf$ of feature $f_i(t)$ --- over $\thr$
|
||||
in multiples of standard deviation $\sigma_{c_i}$.
|
||||
Corresponds to a "reverse" cumulative distribution
|
||||
function of $c_i(t)$. Black solid lines indicate rCDF per
|
||||
kernel $k_i(t)$. Red dashed line indicates rCDF for a
|
||||
normal distribution with $\mu=0$ and $\sigma=1$.
|
||||
}
|
||||
\label{fig:app_thresh-lp_kern-sd}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_full_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
\caption{\textbf{Relation between threshold value and pure-noise feature
|
||||
value for Fig.\,\ref{fig:pipeline_full}.}
|
||||
Proportion of pure-noise kernel response $c_i(t)$ that
|
||||
exceeds threshold value $\thr$ --- which determines the
|
||||
average value $\muf$ of feature $f_i(t)$ --- over $\thr$
|
||||
in multiples of standard deviation $\sigma_{c_i}$.
|
||||
Corresponds to a "reverse" cumulative distribution
|
||||
function of $c_i(t)$. Black solid lines indicate rCDF per
|
||||
kernel $k_i(t)$. Red dashed line indicates rCDF for a
|
||||
normal distribution with $\mu=0$ and $\sigma=1$.
|
||||
}
|
||||
\label{fig:app_full_kern-sd}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_short_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
\caption{\textbf{Relation between threshold value and pure-noise feature
|
||||
value for Fig.\,\ref{fig:pipeline_short}.}
|
||||
Proportion of pure-noise kernel response $c_i(t)$ that
|
||||
exceeds threshold value $\thr$ --- which determines the
|
||||
average value $\muf$ of feature $f_i(t)$ --- over $\thr$
|
||||
in multiples of standard deviation $\sigma_{c_i}$.
|
||||
Corresponds to a "reverse" cumulative distribution
|
||||
function of $c_i(t)$. Black solid lines indicate rCDF per
|
||||
kernel $k_i(t)$. Red dashed line indicates rCDF for a
|
||||
normal distribution with $\mu=0$ and $\sigma=1$.
|
||||
}
|
||||
\label{fig:app_short_kern-sd}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_kernel_sd_perc_field_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
\caption{\textbf{Relation between threshold value and pure-noise feature
|
||||
value for Fig.\,\ref{fig:pipeline_field}.}
|
||||
Proportion of pure-noise kernel response $c_i(t)$ that
|
||||
exceeds threshold value $\thr$ --- which determines the
|
||||
average value $\muf$ of feature $f_i(t)$ --- over $\thr$
|
||||
in multiples of standard deviation $\sigma_{c_i}$.
|
||||
Corresponds to a "reverse" cumulative distribution
|
||||
function of $c_i(t)$. Black solid lines indicate rCDF per
|
||||
kernel $k_i(t)$. Red dashed line indicates rCDF for a
|
||||
normal distribution with $\mu=0$ and $\sigma=1$.
|
||||
}
|
||||
\label{fig:app_field_kern-sd}
|
||||
\end{figure}
|
||||
\end{figure}% Referenced.
|
||||
\FloatBarrier
|
||||
|
||||
|
||||
\begin{figure}[!ht]
|
||||
\centering
|
||||
\includegraphics[width=\textwidth]{figures/fig_invariance_cross_species_thresh_appendix.pdf}
|
||||
\caption{\textbf{}
|
||||
\caption{\textbf{Threshold-dependent intensity invariance of
|
||||
species-specific feature sets.}
|
||||
Same input and processing as in
|
||||
Fig.\,\ref{fig:pipeline_full}, using different
|
||||
kernel-specific threshold values $\thr$ (multiples of
|
||||
pure-noise standard deviation $\sigma_{\eta_i}$ of
|
||||
$c_i(t)$ for $\sca=0$. See also appendix
|
||||
Fig.\,\ref{fig:app_full_kern-sd}). Average value $\muf$ of
|
||||
each feature $f_i(t)$ over $\sca$.
|
||||
}
|
||||
\label{fig:app_cross_species_thresh}
|
||||
\end{figure}
|
||||
\end{figure}% Reference this one!
|
||||
\FloatBarrier
|
||||
|
||||
\end{document}
|
||||
@@ -1,7 +1,7 @@
|
||||
import plotstyle_plt
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from plot_functions import xlabel, ylabel, strip_zeros, letter_subplots
|
||||
from plot_functions import xlabel, ylabel
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
data_path = '../data/inv/noise_env/sd_conversion.npz'
|
||||
@@ -10,16 +10,14 @@ save_path = '../figures/fig_noise_env_sd_conversion_appendix.pdf'
|
||||
# PLOT SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
nrows=2,
|
||||
nrows=1,
|
||||
ncols=1,
|
||||
sharex=True,
|
||||
sharey=True,
|
||||
gridspec_kw=dict(
|
||||
wspace=0,
|
||||
hspace=0.1,
|
||||
left=0.09,
|
||||
hspace=0,
|
||||
left=0.08,
|
||||
right=0.98,
|
||||
bottom=0.08,
|
||||
bottom=0.1,
|
||||
top=0.95,
|
||||
)
|
||||
)
|
||||
@@ -30,81 +28,41 @@ grid_line_kwargs = dict(
|
||||
color='k',
|
||||
lw=0.5,
|
||||
)
|
||||
trial_kwargs = dict(
|
||||
color='k',
|
||||
alpha=0.5,
|
||||
lw=0.5,
|
||||
)
|
||||
line_kwargs = dict(
|
||||
color='black',
|
||||
lw=1,
|
||||
)
|
||||
fill_kwargs = dict(
|
||||
color='k',
|
||||
c='k',
|
||||
lw=0.5,
|
||||
alpha=0.5,
|
||||
)
|
||||
xlabels = dict(
|
||||
bottom='$\\text{scale }\\alpha$',
|
||||
)
|
||||
ylabels = dict(
|
||||
top='$\\sigma_{\\eta}\\,(PLACEHOLDER \\,\\text{realizations})$',
|
||||
bottom='$\\sigma_{\\eta}\\,(\\text{mean}\\,\\pm\\,\\text{SD})$',
|
||||
)
|
||||
xlab = '$\\text{scale }\\alpha$'
|
||||
xlab_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=20,
|
||||
ha='center',
|
||||
va='bottom',
|
||||
)
|
||||
ylab = '$\\sigma_{\\eta}$'
|
||||
ylab_kwargs = dict(
|
||||
x=0,
|
||||
fontsize=20,
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
title_kwargs = dict(
|
||||
t='$\\sigma_{\\text{filt}}\\,=$',
|
||||
x=0.5,
|
||||
y=1,
|
||||
ha='center',
|
||||
va='top',
|
||||
fontsize=20,
|
||||
)
|
||||
letter_kwargs = dict(
|
||||
x=0.005,
|
||||
y=0.99,
|
||||
fontsize=22,
|
||||
ha='left',
|
||||
va='top',
|
||||
)
|
||||
|
||||
# Fetch data:
|
||||
data = dict(np.load('../data/inv/noise_env/sd_conversion.npz'))
|
||||
n = data['n_trials']
|
||||
|
||||
# Adjust parameters:
|
||||
ylabels['top'] = f'$\\sigma_{{\\eta}}\\,({data["n_trials"]}\\text{{ realizations}})$'
|
||||
title_kwargs['t'] += f'$\\,{strip_zeros(data["sd_factor"])}$'
|
||||
|
||||
# Prepare graph:
|
||||
fig, (ax1, ax2) = plt.subplots(**fig_kwargs)
|
||||
fig.suptitle(**title_kwargs)
|
||||
ax1.grid(**grid_line_kwargs)
|
||||
ax1.set_xlim(data['scales'][0], data['scales'][-1])
|
||||
ax1.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
|
||||
ax1.set_ylim(0, 0.1)
|
||||
ylabel(ax1, ylabels['top'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ax2.grid(**grid_line_kwargs)
|
||||
xlabel(ax2, xlabels['bottom'], transform=fig.transFigure, **xlab_kwargs)
|
||||
ylabel(ax2, ylabels['bottom'], transform=fig.transFigure, **ylab_kwargs)
|
||||
letter_subplots((ax1, ax2), **letter_kwargs)
|
||||
fig, ax = plt.subplots(**fig_kwargs)
|
||||
ax.grid(**grid_line_kwargs)
|
||||
ax.set_xlim(data['scales'][0], data['scales'][-1])
|
||||
ax.set_xscale('symlog', linthresh=data['scales'][1], linscale=0.5)
|
||||
ax.set_ylim(0, 0.08)
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(0.02))
|
||||
xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
|
||||
ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
|
||||
|
||||
# Plot individual trials:
|
||||
ax1.plot(data['scales'], data['trials'], **trial_kwargs)
|
||||
|
||||
# Plot mean and spread across trials:
|
||||
ax2.plot(data['scales'], data['mean'], **line_kwargs)
|
||||
ax2.fill_between(data['scales'], data['mean'] - data['spread'], data['mean'] + data['spread'], **fill_kwargs)
|
||||
ax.plot(data['scales'], data['sd_noise'][..., 0], **line_kwargs)
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
|
||||
@@ -45,7 +45,7 @@ save_path = '../figures/fig_features_cross_species.pdf'
|
||||
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[5]
|
||||
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])[4]
|
||||
single_spec_file = True # Only use example files for cross-species comparison
|
||||
equalize_spec_files = False # Prune to minimum available across species
|
||||
n_song = n_spec#None # Limit to n first songs of in-species dataset (None for all)
|
||||
|
||||
@@ -13,9 +13,9 @@ from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target_species = [
|
||||
# 'Chorthippus_biguttulus',
|
||||
# 'Chorthippus_mollis',
|
||||
# 'Chrysochraon_dispar',
|
||||
'Chorthippus_biguttulus',
|
||||
'Chorthippus_mollis',
|
||||
'Chrysochraon_dispar',
|
||||
# 'Euchorthippus_declivus',
|
||||
'Gomphocerippus_rufus',
|
||||
'Omocestus_rufipes',
|
||||
@@ -35,7 +35,15 @@ save_path = '../figures/fig_invariance_cross_species_thresh_appendix.pdf'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
exclude_zero = True
|
||||
thresh_rel = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3])
|
||||
thresh_rel = np.array([
|
||||
# 0,
|
||||
0.5,
|
||||
1,
|
||||
# 1.5,
|
||||
2,
|
||||
# 2.5,
|
||||
3,
|
||||
])
|
||||
|
||||
# SUBSET SETTINGS:
|
||||
types = np.array([1, -1, 2, -2, 3, -3, 4, -4])
|
||||
@@ -53,15 +61,15 @@ fig_kwargs = dict(
|
||||
sharex=True,
|
||||
sharey=True,
|
||||
gridspec_kw=dict(
|
||||
wspace=0.2,
|
||||
hspace=0.75,
|
||||
wspace=0.3,
|
||||
hspace=0.5,
|
||||
left=0.1,
|
||||
right=0.95,
|
||||
bottom=0.08,
|
||||
right=0.97,
|
||||
bottom=0.1,
|
||||
top=0.98,
|
||||
)
|
||||
)
|
||||
inset_x_bounds = [0, -0.5, 1, 0.4]
|
||||
inset_x_bounds = [0, -0.3, 1, 0.25]
|
||||
inset_y_bounds = [1.01, 0, 0.1, 1]
|
||||
|
||||
# PLOT SETTINGS:
|
||||
@@ -162,6 +170,7 @@ y_dist_kwargs = dict(
|
||||
fig, axes = plt.subplots(**fig_kwargs)
|
||||
axes[0, 0].set_ylim(0, 1)
|
||||
axes[0, 0].yaxis.set_major_locator(plt.MultipleLocator(yloc))
|
||||
axes[0, 0].xaxis.set_major_locator(plt.LogLocator(base=10, subs=(1,)))
|
||||
super_xlabel(xlab, fig, axes[-1, 0], axes[-1, -1], **xlab_kwargs)
|
||||
super_ylabel(ylab, fig, axes[0, 0], axes[-1, 0], **ylab_super_kwargs)
|
||||
for ax, species in zip(axes[0, :], target_species):
|
||||
@@ -197,25 +206,27 @@ for i, species in enumerate(target_species):
|
||||
symlog_kwargs = dict(linthresh=scales[scales > 0][0], linscale=0.5)
|
||||
|
||||
# Run through thresholds:
|
||||
for j in range(thresh_rel.size):
|
||||
for j, thresh in enumerate(thresh_rel):
|
||||
ax = axes[j, i]
|
||||
ind = np.nonzero(data['thresh_rel'] == thresh)[0][0]
|
||||
|
||||
# Plot swarm of feature-specific intensity curves:
|
||||
handles = ax.plot(scales, measure[:, :, j], lw=lw['swarm'])
|
||||
handles = ax.plot(scales, measure[:, :, ind], lw=lw['swarm'])
|
||||
assign_colors(handles, config['k_specs'][:, 0], kern_colors)
|
||||
reorder_by_sd(handles, measure[:, :, j])
|
||||
reorder_by_sd(handles, measure[:, :, ind])
|
||||
|
||||
# Plot single compressed intensity curve:
|
||||
compressed = np.median(measure[:, :, j], axis=1)
|
||||
compressed = np.median(measure[:, :, ind], axis=1)
|
||||
ax.plot(scales, compressed, **median_kwargs)
|
||||
|
||||
# Plot distribution of saturation levels:
|
||||
inset = ax.inset_axes(inset_y_bounds)
|
||||
inset.set_ylim(0, 1)
|
||||
inset.axis('off')
|
||||
y_dist(inset, measure[-1, :, j], **y_dist_kwargs)
|
||||
y_dist(inset, measure[-1, :, ind], **y_dist_kwargs)
|
||||
|
||||
# Plot distribution of saturation points:
|
||||
crit_inds = np.array(get_saturation(measure[:, :, j], **plateau_settings)[1])
|
||||
crit_inds = np.array(get_saturation(measure[:, :, ind], **plateau_settings)[1])
|
||||
if np.isnan(crit_inds).sum():
|
||||
print(f'WARNING: No saturation points found for {species} at threshold {thresh_rel[j]}')
|
||||
crit_inds = crit_inds[~np.isnan(crit_inds)].astype(int)
|
||||
@@ -223,12 +234,13 @@ for i, species in enumerate(target_species):
|
||||
inset = ax.inset_axes(inset_x_bounds)
|
||||
inset.set_xlim(scales[0], scales[-1])
|
||||
inset.set_xscale('symlog', **symlog_kwargs)
|
||||
inset.xaxis.set_major_locator(plt.LogLocator(base=10, subs=(1,)))
|
||||
hide_axis(inset, 'left')
|
||||
if j < thresh_rel.size - 1:
|
||||
hide_ticks(inset, 'bottom')
|
||||
x_dist(inset, crit_scales, **x_dist_kwargs)
|
||||
|
||||
if j > 0:
|
||||
if thresh > 0:
|
||||
# Plot single saturation point:
|
||||
crit_ind = get_saturation(compressed, **plateau_settings)[1]
|
||||
crit_scale = scales[crit_ind]
|
||||
@@ -237,6 +249,7 @@ for i, species in enumerate(target_species):
|
||||
# Posthocs:
|
||||
axes[0, 0].set_xscale('symlog', **symlog_kwargs)
|
||||
axes[0, 0].set_xlim(scales[0], scales[-1])
|
||||
axes[0, 0].xaxis.set_major_locator(plt.LogLocator(base=10, subs=(1,)))
|
||||
|
||||
if save_path is not None:
|
||||
fig.savefig(save_path)
|
||||
|
||||
@@ -6,13 +6,13 @@ from plot_functions import xlabel, ylabel
|
||||
from IPython import embed
|
||||
|
||||
# Analysis settings:
|
||||
mode = ['thresh_lp', 'full', 'short', 'field'][3]
|
||||
mode = ['thresh_lp', 'full', 'short', 'field'][0]
|
||||
thresh_path = f'../data/inv/{mode}/thresholds.npz'
|
||||
save_path = f'../figures/fig_kernel_sd_perc_{mode}_appendix.pdf'
|
||||
|
||||
# Plot settings:
|
||||
fig_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
figsize=(32/2.54, 15/2.54),
|
||||
nrows=1,
|
||||
ncols=1,
|
||||
gridspec_kw=dict(
|
||||
@@ -41,8 +41,8 @@ grid_line_kwargs = dict(
|
||||
color='k',
|
||||
lw=0.5,
|
||||
)
|
||||
xlab = '$\\text{multiple of }\\sigma_{k_i}$'
|
||||
ylab = '$P\\,(c_i > \\Theta_i)$'
|
||||
xlab = '$\\Theta_i\\,[\\text{multiples of }\\sigma_{c_i}]$'
|
||||
ylab = '$\\mu_{f_i}\\,\\approx\\,P\\,(c_i > \\Theta_i)$'
|
||||
xlab_kwargs = dict(
|
||||
y=0,
|
||||
fontsize=20,
|
||||
@@ -55,6 +55,8 @@ ylab_kwargs = dict(
|
||||
ha='center',
|
||||
va='top',
|
||||
)
|
||||
xloc = 1
|
||||
yloc = 0.25
|
||||
|
||||
# Load threshold data:
|
||||
data = dict(np.load(thresh_path))
|
||||
@@ -69,6 +71,8 @@ fig, ax = plt.subplots(**fig_kwargs)
|
||||
ax.grid(**grid_line_kwargs)
|
||||
ax.set_xlim(factors[0], factors[-1])
|
||||
ax.set_ylim(-0.01, 1.01)
|
||||
ax.xaxis.set_major_locator(plt.MultipleLocator(xloc))
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(yloc))
|
||||
ylabel(ax, ylab, transform=fig.transFigure, **ylab_kwargs)
|
||||
xlabel(ax, xlab, transform=fig.transFigure, **xlab_kwargs)
|
||||
|
||||
|
||||
@@ -17,7 +17,7 @@ stages = ['filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
|
||||
save_path = '../figures/'
|
||||
|
||||
# GRAPH SETTINGS:
|
||||
fig_kwargs = dict(
|
||||
fig_pre_kwargs = dict(
|
||||
figsize=(32/2.54, 16/2.54),
|
||||
sharex='col',
|
||||
subplot_kw=dict(
|
||||
@@ -28,10 +28,17 @@ fig_kwargs = dict(
|
||||
hspace=0.3,
|
||||
left=0.12,
|
||||
right=0.99,
|
||||
bottom=0.08,
|
||||
top=0.95
|
||||
bottom=0.09,
|
||||
top=0.97
|
||||
),
|
||||
)
|
||||
fig_feat_kwargs = fig_pre_kwargs.copy()
|
||||
fig_feat_kwargs['gridspec_kw'] = fig_pre_kwargs['gridspec_kw'].copy()
|
||||
fig_feat_kwargs['gridspec_kw'].update(dict(
|
||||
left=0.09,
|
||||
wspace=0.15,
|
||||
hspace=0.2,
|
||||
))
|
||||
|
||||
# PLOT SETTINGS:
|
||||
fs = dict(
|
||||
@@ -76,13 +83,15 @@ xlab_kwargs = dict(
|
||||
va='bottom',
|
||||
fontsize=fs['lab_norm'],
|
||||
)
|
||||
ylab_kwargs = dict(
|
||||
ylab_pre_kwargs = dict(
|
||||
x=0.03,
|
||||
rotation=0,
|
||||
ha='center',
|
||||
va='center',
|
||||
fontsize=fs['lab_tex'],
|
||||
)
|
||||
ylab_feat_kwargs = ylab_pre_kwargs.copy()
|
||||
ylab_feat_kwargs['x'] = 0.02
|
||||
xloc = dict(
|
||||
full=2,
|
||||
zoom=0.2
|
||||
@@ -98,42 +107,28 @@ yloc_full = dict(
|
||||
yloc_zoom = dict(
|
||||
filt=0.1,
|
||||
env=0.02,
|
||||
log=50,
|
||||
log=25,
|
||||
inv=10,
|
||||
conv=0.5,
|
||||
feat=1
|
||||
)
|
||||
letter_kwargs = dict(
|
||||
x=0,
|
||||
xref=0,
|
||||
y=1,
|
||||
ha='left',
|
||||
va='bottom',
|
||||
va='center',
|
||||
fontsize=fs['letter'],
|
||||
)
|
||||
zoom_rel = np.array([0.3, 0.4])
|
||||
zoom_rel = np.array([0.295, 0.4])
|
||||
zoom_kwargs = dict(
|
||||
color=3 * (0.85,),
|
||||
zorder=0,
|
||||
linewidth=0
|
||||
)
|
||||
# kernels = np.array([
|
||||
# [1, 0.002],
|
||||
# [1, 0.016],
|
||||
# [-1, 0.004],
|
||||
# [-1, 0.032],
|
||||
# [2, 0.004],
|
||||
# [2, 0.016],
|
||||
# [-2, 0.002],
|
||||
# [-2, 0.032],
|
||||
# [3, 0.008],
|
||||
# [3, 0.032],
|
||||
# [-3, 0.008],
|
||||
# [-3, 0.032],
|
||||
# [4, 0.004],
|
||||
# [4, 0.032],
|
||||
# [-4, 0.004],
|
||||
# [-4, 0.032]
|
||||
# ])
|
||||
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([0.004, 0.032])
|
||||
# sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
|
||||
t = [1, -1, 2, -2, 3, -3, 4, -4]
|
||||
s = [0.004, 0.032]
|
||||
kernels = np.array([[i, j] for i in t for j in s])
|
||||
@@ -162,31 +157,31 @@ for data_path in data_paths:
|
||||
|
||||
|
||||
# PART I: PREPROCESSING STAGE
|
||||
fig, axes = plt.subplots(4, 2, **fig_kwargs)
|
||||
fig, axes = plt.subplots(4, 2, **fig_pre_kwargs)
|
||||
super_xlabel(xlabels['super'], fig, axes[0, 0], axes[0, -1], **xlab_kwargs)
|
||||
[hide_axis(ax, 'bottom') for ax in axes[:-1, :].ravel()]
|
||||
|
||||
# Bandpass-filtered signal:
|
||||
ax_full, ax_zoom = axes[0, :]
|
||||
ylabel(ax_full, ylabels['filt'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ylabel(ax_full, ylabels['filt'], transform=fig.transFigure, **ylab_pre_kwargs)
|
||||
plot_line(ax_full, t_full, data['filt'], c=colors['filt'], lw=lw_full['filt'], yloc=yloc_full['filt'])
|
||||
plot_line(ax_zoom, t_zoom, data['filt'][zoom_mask], c=colors['filt'], lw=lw_zoom['filt'], yloc=yloc_zoom['filt'])
|
||||
|
||||
# Signal envelope:
|
||||
ax_full, ax_zoom = axes[1, :]
|
||||
ylabel(ax_full, ylabels['env'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ylabel(ax_full, ylabels['env'], transform=fig.transFigure, **ylab_pre_kwargs)
|
||||
plot_line(ax_full, t_full, data['env'], ymin=0, c=colors['env'], lw=lw_full['env'], yloc=yloc_full['env'])
|
||||
plot_line(ax_zoom, t_zoom, data['env'][zoom_mask], ymin=0, c=colors['env'], lw=lw_zoom['env'], yloc=yloc_zoom['env'])
|
||||
|
||||
# Logarithmic envelope:
|
||||
ax_full, ax_zoom = axes[2, :]
|
||||
ylabel(ax_full, ylabels['log'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ylabel(ax_full, ylabels['log'], transform=fig.transFigure, **ylab_pre_kwargs)
|
||||
plot_line(ax_full, t_full, data['log'], ymax=0, c=colors['log'], lw=lw_full['log'], yloc=yloc_full['log'])
|
||||
plot_line(ax_zoom, t_zoom, data['log'][zoom_mask], ymax=0, c=colors['log'], lw=lw_zoom['log'], yloc=yloc_zoom['log'])
|
||||
plot_line(ax_zoom, t_zoom, data['log'][zoom_mask], c=colors['log'], lw=lw_zoom['log'], yloc=yloc_zoom['log'])
|
||||
|
||||
# Adapted envelope:
|
||||
ax_full, ax_zoom = axes[3, :]
|
||||
ylabel(ax_full, ylabels['inv'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ylabel(ax_full, ylabels['inv'], transform=fig.transFigure, **ylab_pre_kwargs)
|
||||
plot_line(ax_full, t_full, data['inv'], c=colors['inv'], lw=lw_full['inv'], yloc=yloc_full['inv'])
|
||||
plot_line(ax_zoom, t_zoom, data['inv'][zoom_mask], c=colors['inv'], lw=lw_zoom['inv'], yloc=yloc_zoom['inv'])
|
||||
|
||||
@@ -197,25 +192,17 @@ for data_path in data_paths:
|
||||
ax_zoom.xaxis.set_major_locator(plt.MultipleLocator(xloc['zoom']))
|
||||
indicate_zoom(fig, axes[0, 0], axes[-1, 0], zoom_abs, **zoom_kwargs)
|
||||
indicate_zoom(fig, axes[0, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
|
||||
letter_subplots(axes[:, 0], **letter_kwargs)
|
||||
letter_subplots(axes[:, 0], ref=fig.transFigure, **letter_kwargs)
|
||||
if save_path is not None:
|
||||
fig.savefig(f'{save_path}fig_pre_stages.pdf')
|
||||
|
||||
# Update parameters:
|
||||
fig_kwargs['gridspec_kw'].update(
|
||||
left=0.09,
|
||||
)
|
||||
ylab_kwargs.update(
|
||||
x=0.02,
|
||||
)
|
||||
|
||||
# PART II: FEATURE EXTRACTION STAGE:
|
||||
fig, axes = plt.subplots(3, 2, **fig_kwargs)
|
||||
fig, axes = plt.subplots(3, 2, **fig_feat_kwargs)
|
||||
super_xlabel(xlabels['super'], fig, axes[0, 0], axes[0, -1], **xlab_kwargs)
|
||||
|
||||
# Convolutional filter responses:
|
||||
ax_full, ax_zoom = axes[0, :]
|
||||
ylabel(ax_full, ylabels['conv'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ylabel(ax_full, ylabels['conv'], transform=fig.transFigure, **ylab_feat_kwargs)
|
||||
signal = data['conv'][:, kern_inds]
|
||||
handles = plot_line(ax_full, t_full, signal, lw=lw_full['conv'], yloc=yloc_full['conv'])
|
||||
assign_colors(handles, kern_specs[:, 0], conv_colors)
|
||||
@@ -228,7 +215,7 @@ for data_path in data_paths:
|
||||
|
||||
# Binary responses:
|
||||
ax_full, ax_zoom = axes[1, :]
|
||||
ylabel(ax_full, ylabels['bi'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ylabel(ax_full, ylabels['bi'], transform=fig.transFigure, **ylab_feat_kwargs)
|
||||
signal = data['bi'][:, kern_inds]
|
||||
handles = plot_barcode(ax_full, t_full, signal, lw=lw_full['bi'])
|
||||
assign_colors(handles, kern_specs[:, 0], bi_colors)
|
||||
@@ -237,7 +224,7 @@ for data_path in data_paths:
|
||||
|
||||
# Finalized features:
|
||||
ax_full, ax_zoom = axes[2, :]
|
||||
ylabel(ax_full, ylabels['feat'], transform=fig.transFigure, **ylab_kwargs)
|
||||
ylabel(ax_full, ylabels['feat'], transform=fig.transFigure, **ylab_feat_kwargs)
|
||||
signal = data['feat'][:, kern_inds]
|
||||
handles = plot_line(ax_full, t_full, signal, ymin=0, ymax=1, c=colors['feat'], lw=lw_full['feat'], yloc=yloc_full['feat'])
|
||||
assign_colors(handles, kern_specs[:, 0], feat_colors)
|
||||
@@ -251,7 +238,7 @@ for data_path in data_paths:
|
||||
ax_zoom.xaxis.set_major_locator(plt.MultipleLocator(xloc['zoom']))
|
||||
indicate_zoom(fig, axes[0, 0], axes[-1, 0], zoom_abs, **zoom_kwargs)
|
||||
indicate_zoom(fig, axes[0, 1], axes[-1, 1], zoom_abs, **zoom_kwargs)
|
||||
letter_subplots(axes[:, 0], **letter_kwargs)
|
||||
letter_subplots(axes[:, 0], ref=fig.transFigure, **letter_kwargs)
|
||||
if save_path is not None:
|
||||
fig.savefig(f'{save_path}fig_feat_stages.pdf')
|
||||
plt.show()
|
||||
|
||||
@@ -90,6 +90,12 @@ text_kwargs = dict(
|
||||
ha='right',
|
||||
va='top',
|
||||
)
|
||||
plateau_dot_kwargs = dict(
|
||||
marker='o',
|
||||
markersize=8,
|
||||
markeredgewidth=1,
|
||||
clip_on=False,
|
||||
)
|
||||
|
||||
# Prepare graph:
|
||||
fig, axes = plt.subplots(**fig_kwargs)
|
||||
@@ -111,12 +117,22 @@ for species, ax in zip(target_species, axes):
|
||||
# Plot distribution of saturation points:
|
||||
handles.append(ax.bar(bins, hist, width=bins[1] - bins[0], fc=color, **bar_kwargs))
|
||||
ax.set_ylim(0, hist.max() * 1.05)
|
||||
if species == 'Gomphocerippus_rufus':
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(0.05))
|
||||
else:
|
||||
ax.yaxis.set_major_locator(plt.MultipleLocator(0.03))
|
||||
|
||||
# Indicate mean of distribution:
|
||||
ax.axvline(data['crit_scales'].mean(), **mean_kwargs)
|
||||
# ax.axvline(data['crit_scales'].mean(), **mean_kwargs)
|
||||
|
||||
# Indicate number of songs:
|
||||
ax.text(**text_kwargs, s=f'n = {n_songs}', transform=ax.transAxes)
|
||||
ax.text(**text_kwargs, s=f'n={n_songs}', transform=ax.transAxes)
|
||||
|
||||
# Indicate saturation point of condensed curve:
|
||||
ax.plot(data['crit_scale'], 0, c='w', alpha=1, zorder=5.5,
|
||||
transform=ax.get_xaxis_transform(), **plateau_dot_kwargs)
|
||||
ax.plot(data['crit_scale'], 0, mfc=color, mec='k', alpha=0.75, zorder=6,
|
||||
transform=ax.get_xaxis_transform(), **plateau_dot_kwargs)
|
||||
|
||||
# Posthocs:
|
||||
labels = [shorten_species(species) for species in target_species]
|
||||
|
||||
@@ -1,26 +1,24 @@
|
||||
import glob
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from thunderhopper.filetools import search_files
|
||||
from thunderhopper.modeltools import load_data
|
||||
from thunderhopper.filters import sosfilter
|
||||
from IPython import embed
|
||||
|
||||
# GENERAL SETTINGS:
|
||||
target = 'Omocestus_rufipes'
|
||||
data_path = glob.glob(f'../data/processed/{target}*.npz')[0]
|
||||
data_path = search_files(target, dir='../data/processed/')[0]
|
||||
save_path = '../data/inv/noise_env/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
scales = np.geomspace(0.1, 10000, 200)
|
||||
scales = np.geomspace(0.01, 1000, 100)
|
||||
sd_inputs = np.array([1.0])
|
||||
n_trials = 10
|
||||
tol_to_one = 0.1
|
||||
n_trials = 100
|
||||
|
||||
# EXECUTION:
|
||||
|
||||
# Load signal data:
|
||||
data, config = load_data(data_path, files='filt')
|
||||
signal, rate = data['filt'], config['rate']
|
||||
data, config = load_data(data_path, files='raw')
|
||||
signal, rate = data['raw'], config['rate']
|
||||
|
||||
# Reduce to song segment and normalize:
|
||||
time = np.arange(signal.shape[0]) / rate
|
||||
@@ -28,67 +26,60 @@ start, end = data['songs_0'].ravel()
|
||||
segment = (time >= start) & (time <= end)
|
||||
signal /= signal[segment].std()
|
||||
|
||||
# Get rescaled signals (time, scale):
|
||||
# Rescale signal (time, scale):
|
||||
signal = signal[:, None] * scales[None, :]
|
||||
|
||||
# Prepare storage:
|
||||
if sd_inputs.size > 1:
|
||||
current_match = 0
|
||||
storage = dict(
|
||||
scales=scales,
|
||||
n_trials=n_trials,
|
||||
sd_factor=np.array([0.]),
|
||||
trials=np.zeros((scales.size, n_trials), dtype=float),
|
||||
mean=np.zeros(scales.size, dtype=float),
|
||||
spread=np.zeros(scales.size, dtype=float),
|
||||
)
|
||||
sd_noise = np.zeros((scales.size, n_trials, sd_inputs.size), dtype=float)
|
||||
|
||||
# Analyze piece-wise:
|
||||
rng = np.random.default_rng()
|
||||
for i, sigma in enumerate(sd_inputs):
|
||||
print(f'Testing SD: {sigma:.3f} ...')
|
||||
|
||||
# Add Gaussian noise of given SD to rescaled signals (time, scale, trial):
|
||||
mix = signal[..., None] + rng.normal(0, sigma, (*signal.shape, n_trials))
|
||||
# Prepare trial storage:
|
||||
sd_trials = np.zeros((segment.sum(), scales.size, n_trials), dtype=float)
|
||||
|
||||
# Get mixture envelopes (time, scale, trial):
|
||||
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
|
||||
padtype='even', padlen=config['padlen'])[segment, ...]
|
||||
# Run trials:
|
||||
for j in range(n_trials):
|
||||
# Mix signals with white noise of target SD:
|
||||
mix = signal + rng.normal(0, sigma, signal.shape)
|
||||
|
||||
# Process mixture:
|
||||
mix = sosfilter(mix, rate, config['bp_fcut'], 'bp',
|
||||
padtype='fixed', padlen=config['padlen'])
|
||||
mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
|
||||
padtype='even', padlen=config['padlen'])
|
||||
|
||||
# Log current trial:
|
||||
sd_trials[..., j] = mix[segment, :]
|
||||
|
||||
# Get noise remainders of mean over trials:
|
||||
mix -= mix.mean(axis=-1, keepdims=True)
|
||||
sd_trials -= sd_trials.mean(axis=-1, keepdims=True)
|
||||
|
||||
# Estimate noise SD:
|
||||
sd = mix.std(axis=0)
|
||||
# Average SD over trials:
|
||||
mean_sd = sd.mean(axis=-1)
|
||||
sd_noise[:, :, i] = sd_trials.std(axis=0)
|
||||
|
||||
# Log single-run results:
|
||||
if sd_inputs.size == 1:
|
||||
storage = dict(
|
||||
scales=scales,
|
||||
n_trials=n_trials,
|
||||
sd_factor=sigma,
|
||||
trials=sd,
|
||||
mean=mean_sd,
|
||||
spread=sd.std(axis=-1),
|
||||
)
|
||||
break
|
||||
# # Add Gaussian noise of given SD to rescaled signals (time, scale, trial):
|
||||
# mix = signal[..., None] + rng.normal(0, sigma, (*signal.shape, n_trials))
|
||||
|
||||
# Update multi-run results if better than previous:
|
||||
n_match = (np.abs(1 - mean_sd) <= tol_to_one).sum()
|
||||
if n_match > current_match:
|
||||
print(f'Found better SD: {sigma:.3f} with {n_match} matches (previous: {current_match})')
|
||||
storage['sd_factor'][0] = sigma
|
||||
storage['trials'][:, :] = sd
|
||||
storage['mean'][:] = mean_sd
|
||||
storage['spread'][:] = sd.std(axis=-1)
|
||||
current_match = n_match
|
||||
del mix
|
||||
del signal
|
||||
# # Get mixture envelopes (time, scale, trial):
|
||||
# mix = sosfilter(np.abs(mix), rate, config['env_fcut'], 'lp',
|
||||
# padtype='even', padlen=config['padlen'])[segment, ...]
|
||||
|
||||
# # Get noise remainders of mean over trials:
|
||||
# mix -= mix.mean(axis=-1, keepdims=True)
|
||||
|
||||
# # Estimate noise SD:
|
||||
# sd_noise[:, :, i] = mix.std(axis=0)
|
||||
|
||||
if save_path is not None:
|
||||
np.savez(save_path + 'sd_conversion.npz', **storage)
|
||||
archive = dict(
|
||||
scales=scales,
|
||||
sd_input=sd_inputs,
|
||||
sd_noise=sd_noise,
|
||||
)
|
||||
np.savez(save_path + 'sd_conversion.npz', **archive)
|
||||
|
||||
print('Done.')
|
||||
embed()
|
||||
|
||||
@@ -14,7 +14,8 @@ target_species = [
|
||||
'Omocestus_rufipes',
|
||||
'Pseudochorthippus_parallelus',
|
||||
]
|
||||
search_path = '../data/inv/log_hp/collected/'
|
||||
collect_path = '../data/inv/log_hp/collected/'
|
||||
condense_path = '../data/inv/log_hp/condensed/'
|
||||
save_path = '../data/inv/log_hp/saturation/'
|
||||
|
||||
# ANALYSIS SETTINGS:
|
||||
@@ -32,7 +33,7 @@ pad = 0.05
|
||||
# PREPARATION:
|
||||
if compute_hist:
|
||||
species_scales = []
|
||||
min_scale, max_scale = [], []
|
||||
min_scale, max_scale = np.inf, -np.inf
|
||||
archives = [{} for _ in target_species]
|
||||
|
||||
# EXECUTION:
|
||||
@@ -40,31 +41,48 @@ for i, species in enumerate(target_species):
|
||||
print(f'Processing {species}')
|
||||
|
||||
# Load accumulated invariance data:
|
||||
path = search_files(species, dir=search_path)[0]
|
||||
path = search_files(species, dir=collect_path)[0]
|
||||
data, config = load_data(path, ['scales', 'measure_inv'])
|
||||
|
||||
# Find upper saturation point per song file:
|
||||
crit_inds = np.array(get_saturation(data['measure_inv'], **plateau_settings)[1])
|
||||
crit_scales = data['scales'][crit_inds]
|
||||
|
||||
# Load condensed invariance data:
|
||||
path = search_files(species, incl=['noise', 'norm-base'], dir=condense_path)[0]
|
||||
data, _ = load_data(path, ['scales', 'mean_inv'])
|
||||
|
||||
# Find single upper saturation point of condensed curve:
|
||||
crit_ind = get_saturation(data['mean_inv'].mean(axis=-1), **plateau_settings)[1]
|
||||
crit_scale = data['scales'][crit_ind]
|
||||
|
||||
# Output options:
|
||||
if not compute_hist:
|
||||
# Save species data immediately:
|
||||
archive = dict(crit_inds=crit_inds, crit_scales=crit_scales, scales=data['scales'])
|
||||
archive = dict(
|
||||
scales=data['scales'],
|
||||
crit_inds=crit_inds,
|
||||
crit_scales=crit_scales,
|
||||
crit_ind=crit_ind,
|
||||
crit_scale=crit_scale,
|
||||
)
|
||||
save_data(save_path + species, archive, config, overwrite=True)
|
||||
continue
|
||||
|
||||
# Log but don't save data yet:
|
||||
archives[i]['crit_inds'] = crit_inds
|
||||
archives[i]['crit_scales'] = crit_scales
|
||||
archives[i]['scales'] = data['scales']
|
||||
min_scale.append(crit_scales.min())
|
||||
max_scale.append(crit_scales.max())
|
||||
min_scale = min(crit_scales.min(), min_scale)
|
||||
max_scale = max(crit_scales.max(), max_scale)
|
||||
archives[i].update(
|
||||
scales=data['scales'],
|
||||
crit_inds=crit_inds,
|
||||
crit_scales=crit_scales,
|
||||
crit_ind=crit_ind,
|
||||
crit_scale=crit_scale,
|
||||
)
|
||||
|
||||
# Optional histogram:
|
||||
if compute_hist:
|
||||
# Generated shared histogram edges:
|
||||
min_scale, max_scale = min(min_scale), max(max_scale)
|
||||
# Generated shared bin edges:
|
||||
pad *= (max_scale - min_scale)
|
||||
edges = np.linspace(max(0, min_scale - pad), max_scale + pad, bins + 1)
|
||||
centers = edges[:-1] + np.diff(edges) / 2
|
||||
|
||||
Reference in New Issue
Block a user