Bio-Inspired Optimization of Parametric Onset Detectors
Onset detectors are used to recognize the beginning of musical
events in audio signals. Manual parameter tuning for onset detectors is a time consuming task, while existing automated approaches often maximize only a single performance metric. These
automated approaches cannot be used to optimize detector algorithms for complex scenarios, such as real-time onset detection
where an optimization process must consider both detection accuracy and latency. For this reason, a flexible optimization algorithm
should account for more than one performance metric in a multiobjective manner. This paper presents a generalized procedure for
automated optimization of parametric onset detectors. Our procedure employs a bio-inspired evolutionary computation algorithm
to replace manual parameter tuning, followed by the computation
of the Pareto frontier for multi-objective optimization. The proposed approach was evaluated on all the onset detection methods
of the Aubio library, using a dataset of monophonic acoustic guitar
recordings. Results show that the proposed solution is effective in
reducing the human effort required in the optimization process: it
replaced more than two days of manual parameter tuning with 13
hours and 34 minutes of automated computation. Moreover, the
resulting performance was comparable to that obtained by manual
optimization.