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  4. Fundamental Frequency Contour Classification: A Comparison Between Hand-Crafted and CNN-Based Features
 
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2019
Conference Paper
Title

Fundamental Frequency Contour Classification: A Comparison Between Hand-Crafted and CNN-Based Features

Abstract
In this paper, we evaluate hand-crafted features as well as features learned from data using a convolutional neural network (CNN) for different fundamental frequency classification tasks. We compare classification based on full (variable-length) contours and classification based on fixed-sized subcontours in combination with a fusion strategy. Our results show that hand-crafted and learned features lead to comparable results for both classification scenarios. Aggregating contour-level to file-level classification results generally improves the results. In comparison to the hand-crafted features, our examination indicates that the CNN-based features show a higher degree of redundancy across feature dimensions, where multiple filters (convolution kernels) specialize on similar contour shapes.
Author(s)
Abeßer, Jakob  
Müller, Meinard  
Mainwork
ICASSP 2019, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings  
Conference
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2019  
DOI
10.1109/ICASSP.2019.8682252
Language
English
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Keyword(s)
  • melody contour analysis

  • automatic music analysis

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