summaryrefslogtreecommitdiff
diff options
context:
space:
mode:
authorAndré Nusser <andre.nusser@googlemail.com>2020-02-03 09:35:08 +0100
committerAndré Nusser <andre.nusser@googlemail.com>2020-02-03 09:35:08 +0100
commitcb4f358ffaf86759b788db07a4d56e5d965b0aa1 (patch)
tree17e0a2ba38b7bcbf15ac7413964242bffd2d3907
parent3ef15a3cc62d7f4c361d04d15c5b97be030f7581 (diff)
Clarify setting.
-rw-r--r--sampling_alg_lac2020/LAC-20.tex13
1 files changed, 11 insertions, 2 deletions
diff --git a/sampling_alg_lac2020/LAC-20.tex b/sampling_alg_lac2020/LAC-20.tex
index 7ee146d..8a56d1f 100644
--- a/sampling_alg_lac2020/LAC-20.tex
+++ b/sampling_alg_lac2020/LAC-20.tex
@@ -335,7 +335,16 @@ To the best of our knowledge, this is the first academic article that deals with
\todobent{Talk about how the drum kit samples are usually created; very briefly.}
\todobent{Talk about loudness computation of samples.}
\todo{Mathematical basics (if there are any important ones).}
+
\todo{Formalize the setting, i.e.\ what is the input/output of our algorithm?}
+\subsection{Setting}
+We now describe the setting in which we want to choose the samples. We are given:
+\begin{itemize}
+ \item a drum kit consisting of a set of instruments $I$
+ \item for each instrument $i \in I$, we are given an input sample set $S_i$
+ \item each sample $s \in S_i$ is already labeled with a power value $p_s \in \mathbb{R}^+$
+\end{itemize}
+After reading the drum kit, requests of the form $(i, p) \in I \times \mathbb{R}^+$ arrive. We want to answer these requests by choosing the best sample from $S_i$ for the power value $p$.
% \todoandre{Make terminology and notation clear and check for consistency in the document.}
\subsection{Notation and Terminology}
@@ -398,11 +407,11 @@ just adds some noise to the process to make it non-deterministic and thus avoid
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}
- \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
+ \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{$s' \in \{ s'' \mid s'' \text{ is sample of instrument }I\}$}
+ \For{$s' \in S_i$}
\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 $f_{\min} \gets v$