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

 
: Abeßer, Jakob; Müller, Meinard

:

Sanei, Saeid (General Chair) ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
ICASSP 2019, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings : May 12-17, 2019, Brighton
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-4799-8131-1
ISBN: 978-1-4799-8132-8
S.486-490
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) <44, 2019, Brighton>
Englisch
Konferenzbeitrag
Fraunhofer IDMT ()
melody contour analysis

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.

: http://publica.fraunhofer.de/dokumente/N-552397.html