Comparison of parameter optimization techniques for a music tone onset detection algorithm

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.