All that jazz in the random forest

Authors:
Elżbieta Kubera;Miron B. Kursa;Witold R. Rudnicki;Radosław Rudnicki;Alicja A. Wieczorkowska
Affiliations:
University of Life Sciences in Lublin, Lublin, Poland;Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland;Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland;The University of York, Department of Music, Heslington, York, UK;Polish-Japanese Institute of Information Technology, Warsaw, Poland
Venue:
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Year:
2011

Citing 11
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Abstract

In this paper, we address the problem of automatic identification of instruments in audio records, in a frame-by-frame manner. Random forests have been chosen as a classifier. Training data represent sounds of selected instruments which originate from three commonly used repositories, namely McGill University Master Samples, The University of IOWA Musical Instrument Samples, and RWC, as well as from recordings by one of the authors. Testing data represent audio records especially prepared for research purposes, and then carefully labeled (annotated). The experiments on identification of instruments on frame-by-frame basis and the obtained results are presented and discussed in the paper.