Minor fixes.

1 files 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.
 
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