Exploration of techniques for automatic labeling of audio drum tracks’ instruments

We report on the progress of current work regarding the automatic recognition of percussive instruments embedded in audio excerpts of performances on drum sets. Content-based transformation of audio drum tracks and loops requires the identification of the instruments that are played in the sound file. Some supervised and unsupervised techniques are examined in this paper, and classification results for a small number of classes are discussed. In order to cope with the issue of classifying percussive events embedded in continuous audio streams, we rely on a method based on an automatic adaptation of the analysis frame size to the smallest metrical pulse, called the “tick”. The success rate with some of the explored techniques has been quite good (around 80%), but some enhancements are still needed in order to be able to accurately classify sounds in real applications’ conditions.