Analysis and automatic recognition of Human BeatBox sounds: A comparative study

Abstract

“Human BeatBox” (HBB) is a newly expanding contemporary singing style where the vocalist imitates drum beats percussive sounds as well as pitched musical instrument sounds. Drum sounds typically use a notation based on plosives and fricatives, and instrument sounds cover vocalisations that go beyond spoken language vowels. HBB hence constitutes an interesting use case for expanding techniques initially developed for speech processing, with the goal of automatically annotating performances as well as developing new sound effects dedicated to HBB performers. In this paper, we investigate three complementary aspects of HBB analysis: pitch tracking, onset detection, and automatic recognition of sounds and instruments. As a first step, a new high-quality HBB audio database has been recorded, carefully segmented and annotated manually to obtain a ground truth reference. Various pitch tracking and onset detection methods are then compared and assessed against this reference. Finally, Hidden Markov Models are evaluated, together with an exploration of their parameters space, for the automatic recognition of different types of sounds. This study exhibits very encouraging experimental results.