Delayed decision-making in real-time beatbox percussion classification

Abstract

Real-time classification applied to a vocal percussion signal holds potential as an interface for live musical control. In this article we propose a novel approach to resolving the tension between the needs for low-latency reaction and reliable classification, by deferring the final classification decision until after a response has been initiated. We introduce a new dataset of annotated human beatbox recordings, and use it to study the optimal delay for classification accuracy. We then investigate the effect of such delayed decision-making on the quality of the audio output of a typical reactive system, via a MUSHRA-type listening test. Our results show that the effect depends on the output audio type: for popular dance/pop drum sounds the acceptable delay is on the order of 12-35 ms. © 2010 Taylor & Francis.