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Computational Intelligence
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@MISC{A_computationalintelligence,
author = {Maximos A and Andreas Floros and Michael N. Vrahatis and Nikolaos Kanellopoulos},
title = {Computational Intelligence},
year = {}
}
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Abstract
Real–time transcription of drum signals is an emerging area of research. Several applications for music education and commercial use can utilize such algorithms and allow for an easy-to-use way to interpret drum signals in real–time. The paper at hand proposes a system that performs real–time drums transcription. The proposed system consists of two subsystems, the real–time separation and the training module. The real–time separation module is based on the use of characteristic filters, combining simple bandpass filtering and amplification, a fact that diminishes computational cost and potentially renders it suitable for implementation on hardware. The training module employs Differential Evolution to create generations of characteristic filter combinations that optimally separate a set of given drum sources. Initial experimental results indicate that the proposed system is relatively accurate rendering it convenient for realtime hardware implementations targeted to a wide range of applications.
Keyphrases
computational intelligence proposed system training module drum signal several application simple bandpass filtering real time separation module real time separation wide range characteristic filter combination real time drum transcription characteristic filter real time transcription drum source real time easy-to-use way initial experimental result computational cost realtime hardware implementation commercial use differential evolution music education
