Model based optimization of music onset detection

Abstract

In this paper a comprehensive online music onset detection algorithm is introduced where - in contrast to many other relevant publications - 14 important algorithm parameters are optimized simultaneously. For solving the optimization problem we derive an extensive tool for iterative model based optimization. In each iteration, a very time consuming evaluation has to be per- formed on a large music data base. To speed up this procedure, the expected performance of each newly proposed setting is estimated in a pretest on a representative part of the data so that just very promising points are evaluated on all data. We compare different variants of the classical and the fast optimization strategies with respect to the F-values of their best identified parameter settings. The performance of the fast approach appears to be competitive with the classical one while saving more than 80% of music piece evaluations on average. Our best found parameter settings, both for online and offline onset detection, are mainly in accordance with the usual choices in the state- of-the art literature concerning, e.g., the spectral flux detection function or preferences for window length and overlap. However, we also found unexpected results. For example, the adaptive whitening pre-processing step showed no effect.