Selecting small audio feature sets in music classification by means of asymmetric mutation

Abstract

Classification of audio recordings is often based on audio-signal features. The number of available variables is usually very large. For successful categorization in e.g. genres, substyles or personal preferences small, but very predictive feature sets are sought. A further challenge is to solve this feature selection problem at least approximately with short run lengths to reduce the high computational load. We pursue this goal by applying asymmetric mutation operators in simple evolutionary strategies, which are further enhanced by mixing in greedy search operators. The resulting algorithm is reliably better than any of these approaches alone and in most cases clearly better than a deterministic greedy strategy. © 2010 Springer-Verlag.