From 2387a4fc1dd833c0f0ac5e356bc72c4f03e75c7f Mon Sep 17 00:00:00 2001 From: Bent Bisballe Nyeng Date: Sun, 9 Feb 2020 13:33:36 +0100 Subject: Minor fixes. --- sampling_alg_lac2020/LAC-20.tex | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/sampling_alg_lac2020/LAC-20.tex b/sampling_alg_lac2020/LAC-20.tex index 12c7d69..4acd2db 100644 --- a/sampling_alg_lac2020/LAC-20.tex +++ b/sampling_alg_lac2020/LAC-20.tex @@ -301,10 +301,13 @@ the group corresponding to the input velocity: \end{verbatim}} This algorithm did not give good results in small samplesets so later -an improved algorithm was introduced which was instead on normal +an improved algorithm was introduced which was instead based on normal distributed random numbers and with power values for each sample in the set. +A prerequisite for this new algorithm is that the power of each sample is +stored along with the sample data of each sample. + The power values of a drum kit are floating point numbers without any restrictions but assumed to be positive. Then the input value $l$ is mapped using the canonical bijections between $[0,1]$ and @@ -313,7 +316,7 @@ amount?}. We call this new value $p$. Now the real sample selection algorithm starts. We select a value $p'$ drawn normal distributed at random from $\mathcal{N}(p', \sigma^2)$, -where the mean value, $\mu$, set to the input value $l$ and +where the mean value, $\mu$, is set to the input value $l$ and and the stddev, $\sigma$, is a parameter controlled by the user expressed in fractions of the size and span of the sampleset. Now we simply find the sample $s$ with the power $q$ which is closest @@ -332,8 +335,6 @@ iterations, we just return the last played sample. / mean \end{verbatim}} -In order to make this new algorithm the power of each sample must be -present along with the sample data. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -- cgit v1.2.3