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  4. Automatic string detection for bass guitar and electric guitar
 
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2012
Conference Paper
Title

Automatic string detection for bass guitar and electric guitar

Abstract
In this paper, we present a feature-based approach to automatically estimate the string number in recordings of the bass guitar and the electric guitar. We perform different experiments to evaluate the classification performance on isolated note recordings. First, we analyze how factors such as the instrument, the playing style, and the pick-up settings affect the performance of the classification system. Second, we investigate, how the classification performance can be improved by rejecting implausible classifications as well as aggregating the classification results over multiple adjacent time frames. The best results we obtained are f-measure values of F = .93 for the bass guitar (4 classes) and F = .90 for the electric guitar (6 classes).
Author(s)
Abeßer, Jakob  
Mainwork
Music and emotions. Proceedings  
Conference
International Symposium on Computer Music Modeling and Retrieval (CMMR) 2012  
Link
Link
DOI
10.1007/978-3-642-41248-6_18
Language
English
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Keyword(s)
  • M2D

  • SMT

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