Selecting Groups of Audio Features by Statistical Tests and the Group Lasso

Abstract:
In this paper we aim at discriminating between two musical instruments by means of different groups of audio features, namely absolute amplitude envelope in the time domain as well as MFCC, pitchless periodogram and simplified spectral envelope in the spectral domain. For this task we utilize common statistical classification algorithms and perform statistical tests to evaluate whether the discriminating power of certain subsets of feature groups dominates other group subsets. We also examine if it is possible to directly select a useful set of groups by applying logistic regression regularized by a group lasso penalty structure. Specifically, we apply our methods to a data set of single piano and guitar tones.