From c40280699582d85be898c6feba44e8d0674e3219 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Andr=C3=A9=20Nusser?= Date: Sun, 2 Feb 2020 14:30:27 +0100 Subject: First version of algorithm section, except one TODO. --- sampling_alg_lac2020/LAC-20.tex | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/sampling_alg_lac2020/LAC-20.tex b/sampling_alg_lac2020/LAC-20.tex index a16f82d..fc58394 100644 --- a/sampling_alg_lac2020/LAC-20.tex +++ b/sampling_alg_lac2020/LAC-20.tex @@ -154,6 +154,8 @@ %\usepackage{showframe} \usepackage{algorithm} \usepackage{algpseudocode} +\renewcommand{\algorithmicrequire}{\textbf{Input:}} +\renewcommand{\algorithmicensure}{\textbf{Output:}} % nice theorem and proof environments. taken from Ema's template \theoremstyle{plain} @@ -343,12 +345,23 @@ The third therm, namely \] just adds some noise to the process to make it non-deterministic and thus avoid patterns in the selection as mentioned in Section \ref{sec:requirements}. -\todoandre{Maybe add some pseudo-code to make things easier to understand?} -Algorithm \ref{alg:sampling} shows the pseudo code for the sample selection algorithm. - +% \todoandre{Maybe add some pseudo-code to make things easier to understand?} +We already explained the core part of the sample selection algorithm. The remainder is now straight-forward. We simply evaluate the objective function for each sample and then pick the one with the smallest value. For completeness, Algorithm \ref{alg:sampling} shows the pseudo code for the sample selection algorithm. \begin{algorithm} \begin{algorithmic} - \State bla + \Require Requested power $p$, Instrument $I$, current time step $t$, parameters $\alpha, \beta, \gamma$, and array $\mathit{last}$ with the time points a sample has been played last + \Ensure Sample $s$ + \State $s = \text{undefined}$ + \State $f_{\min} = \infty$ + \For{$s' \in \{ s'' \mid s'' \text{ is sample of instrument }I\}$} + \State $v \gets \alpha \cdot \left( \frac{p-p_{s'}}{p_{\max} - p_{\min}}\right)^2 + \beta \cdot \left( 1 + \frac{t - \mathit{last}[s']}{S}\right)^{-1} + \gamma \cdot r(s',t)$ + \If{$v < f_{\min}$} + \State $f_{\min} \gets v$ + \State $s \gets s'$ + \EndIf + \EndFor + \State $\mathit{last}[s] = t$ + \State \Return $s$ \end{algorithmic} \caption{This is the high-level code of the sampling function.} \label{alg:sampling} @@ -374,8 +387,9 @@ Note that the worst-case complexity of evaluating the objective function is line \todoandre{Recapitulate what was done in this paper. Highlight some of the difficulties and surprises.} \todoandre{List future work: transforming the loudness space; refine the objective function; adapt algorithm to other instruments/settings; study to see what sounds good to people and do they actually hear the difference?} -\section{Acknowledgements} -\todo{Thank people for testing?} +% \section{Acknowledgements} +% \todo{Thank people for testing?} +% \todo{Thank people for proof-reading?} %\newpage \nocite{*} -- cgit v1.2.3