Time efficient optimization of instance based problems with application to tone onset detection
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Date
2016
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Abstract
A time efficient optimization technique for instance based problems is proposed,
where for each parameter setting the target function has to be evaluated on a
large set of problem instances. Computational time is reduced by beginning with
a performance estimation based on the evaluation of a representative subset of
instances. Subsequently, only promising settings are evaluated on the whole
data set.
As application a comprehensive music onset detection algorithm is introduced
where several numerical and categorical algorithm parameters are optimized
simultaneously. Here, problem instances are music pieces of a data base.
Sequential model based optimization is an appropriate technique to solve this
optimization problem. The proposed optimization strategy is compared to the
usual model based approach with respect to the goodness measure for tone onset
detection. The performance of the proposed method appears to be competitive
with the usual one while saving more than 84% of instance evaluation time
on average. One other aspect is a comparison of two strategies for handling
categorical parameters in Kriging based optimization.
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Keywords
model based optimization, categorical parameters, onset detection, Kriging, instance optimization