On Drum Playing Technique Detection in Polyphonic Mixtures

In this paper, the problem of drum playing technique detection in polyphonic mixtures of music is addressed. We focus on the identification of 4 rudimentary techniques: strike, buzz roll, flam, and drag. The specifics and the challenges of this task are being discussed, and different sets of features are compared, including various features extracted from NMF-based activation functions, as well as baseline spectral features. We investigate the capabilities and limitations of the presented system in the case of real-world recordings and polyphonic mixtures. To design and evaluate the system, two datasets are introduced: a training dataset generated from individual drum hits, and additional annotations of the well-known ENST drum dataset minus one subset as test dataset. The results demonstrate issues with the traditionally used spectral features, and indicate the potential of using NMF activation functions for playing technique detection, however, the performance of polyphonic music still leaves room for future improvement.