Stochastic Feature Selection in Support Vector Machine Based Instrument Recognition

摘要

Automatic instrument recognition is an important task in musical applications. In this paper we concentrate on the recognition of electronic drum sounds from a large commercially available drum sound library. The recognition task can be formulated as classification problem. Each sample is described by one hundred temporal and spectral features. Support Vector Machines turn out to be an excellent choice for this classification task. Furthermore, we concentrate on the stochastic optimization of a feature subset using evolution strategies and compare the results to the classifier that has been trained on the complete feature set.