Abstract
Live performance situations can lead to degradations in the vocal signal from a typical microphone, such as ambient noise or echoes due to feedback. We investigate the robustness of continuousvalued timbre features measured on vocal signals (speech, singing, beatboxing) under simulated degradations. We also consider nonparametric dependencies between features, using information theoretic measures and a feature-selection algorithm. We discuss how robustness and independence issues reflect on the choice of acoustic features for use in constructing a continuous-valued vocal timbre space. While some measures (notably spectral crest factors) emerge as good candidates for such a task, others are poor, and some features such as ZCR exhibit an interaction with the type of voice signal being analysed.