Automatic subgrouping of multitrack audio
Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack are summed to a single audio bus. This is done so that the mix engineer can apply signal processing to an entire subgroup, speed up the mix work flow and manipulate a number of audio tracks at once. In this work, we investigate which audio features from a set of 159 can be used to automatically subgroup multitrack audio. We determine a subset of audio features from the original 159 audio features to use for automatic subgrouping, by performing feature selection using a Random Forest classifier on a dataset of 54 individual multitracks. We show that by using agglomerative clustering on 5 test multitracks, the entire set of audio features incorrectly clusters 35.08% of the audio tracks, while the subset of audio features incorrectly clusters only 7.89% of the audio tracks. Furthermore, we also show that using the entire set of audio features, ten incorrect subgroups are created. However, when using the subset of audio features, only five incorrect subgroups are created. This indicates that our reduced set of audio features provides a significant increase in classification accuracy for the creation of subgroups automatically.