The use of electronic drum samples is widespread in contemporary music productions, with music producers having an unprecedented number of samples available to them. The development of new tools to assist users organizing and managing libraries of this type requires comprehensive audio analysis that is distinct from that used for general classification or onset detection tasks. In this paper 4230 kick and snare samples, representing 250 individual electronic drum machines are evaluated. Samples are segmented into different lengths and analyzed using comprehensive audio feature analysis. Audio classification is used to evaluate and compare the effect of this time segmentation and establish the overall effectiveness of the selected feature set. Results demonstrate that there is improvement in classification scores when using time segmentation as a pre-processing step.
Shier, Jordie; McNally, Kirk; Tzanetakis, George
Affiliations: University of Victoria, Victoria, Canada; University of Victoria, School of Music, Victoria, BC, Canada; University of Victoria, Victoria, BC, Canada(See document for exact affiliation information.)
AES Convention: 143 (October 2017) Paper Number: 9887
Publication Date: October 8, 2017
Subject: Applications in Audio
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