I'm trying to gain some insight into acoustic voice data composed of 19 features. I want to understand what features contribute most for classification.
ADDED: Most features are related with the fundamental frequency stability. In particular I'm using voice shimmer and jitter and some related calculations.
I'm trying to use MRMR (max relevance min redundancy), but I would like to compare with some other options. ADDED: I have tried to use FeatureMiner (http://featureselection.asu.edu/index.php) which provides some interesting algorithms implementations. However many of them use deprecated Python functions and require some effort to work properly.
Are there any popular tools for these purposes?