An open-source drum transcription system for Pure Data and Max MSP
Marius Miron, Matthew E. P. Davies, Fabien Gouyon · 2013
This paper presents a drum transcription algorithm adjusted to the constraints of real-time audio. We introduce an instance filtering (IF) method using sub-band onset detection, which improves the performance of a system having at its core a feature-based K-nearest neighbor classifier (KNN). The architecture proposed allows for adapting different parts of the algorithm for either bass drum, snare drum or hi-hat cymbals. The open-source system is implemented in the graphic programming languages Pure Data (PD) and Max MSP, and aims to work with a large variety of drum sets. We evaluated its performance on a database of audio samples generated from a well known collection of midi drum loops randomly matched with a diverse collection of drum sets. Both of the evaluation stages, testing and validation, show an improvement in the performance when using the instance filtering algorithm.