Abstract
In this paper, we present a feature-based approach for the classification of different playing techniques in bass guitar recordings. The applied audio features are chosen to capture typical instrument sounds induced by 10 different playing techniques. A novel database that consists of approx. 4300 isolated bass notes was assembled for the purpose of evaluation. The usage of domain-specific features in a combination of feature selection and feature space transformation techniques improved the classification accuracy by over 27% points in comparison to a state-of-the-art baseline system. Classification accuracy reached 93.25% and 95.61% for the recognition of plucking and expression styles respectively.