Stem audio mixing as a content-based transformation of audio features
Marco A. Martínez Ramírez, Joshua D. Reiss · 2017
Multitrack audio mixing is an essential part of music production and one of the first steps consist on processing individual stems from raw recordings. In this paper, we investigate this stage as a content-based transformation. We explore which audio features are relevant to interpret this specific process and which set of features gets modified by the mixing of stems in the most consistent way. We show that the number of features can be reduced with a procedure based on the permutation importance method of random forest classifiers. Thus, the selected audio features are used to train various classification models and we analyse which set of features lead to a better classification accuracy. We conclude that the underlying characteristics of manipulating raw recordings into individual stems can be described by this selected set of features.