Comparison of parameter optimization techniques for a music tone onset detection algorithm
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Date
2012-10-15
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Abstract
Design of experiments is an established approach to parameter optimization
for industrial processes. In many computer applications, however, it is usual
to optimize the parameters via genetic algorithms or, recently, via sequential
parameter optimization techniques. The main idea of this work is to analyse
and compare parameter optimization approaches which are usually applied
in industry with those applied for computer optimization tasks using the example
of a tone onset detection algorithm. The optimal algorithm parameter
setting is sought in order to get the best onset detection accuracy.
We vary in our work essential options of the parameter optimization
strategies like size and constitution of the initial designs in order to assess
their in uence on the evaluation results. Furthermore we test how the instrumentation
and the tempo of music pieces affect the optimal parameter
setting of the onset detection algorithm.
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Keywords
design of experiments, sequential parameter optimization, tone onset detection