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BAMMER, R. DÖRFLER, M. HARÁR, P.
Original Title
Gabor frames and deep scattering networks in audio processing
Type
journal article in Web of Science
Language
English
Original Abstract
This paper introduces Gabor scattering, a feature extractor based on Gabor frames and Mallat's scattering transform. By using a simple signal model for audio signals specific properties of Gabor scattering are studied. It is shown that for each layer, specific invariances to certain signal characteristics occur. Furthermore, deformation stability of the coefficient vector generated by the feature extractor is derived by using a decoupling technique which exploits the contractivity of general scattering networks. Deformations are introduced as changes in spectral shape and frequency modulation. The theoretical results are illustrated by numerical examples and experiments. Numerical evidence is given by evaluation on a synthetic and a "real" data set, that the invariances encoded by the Gabor scattering transform lead to higher performance in comparison with just using Gabor transform, especially when few training samples are available.
Keywords
machine learning; scattering transform; Gabor transform; deep learning; time-frequency analysis; CNN;
Authors
BAMMER, R.; DÖRFLER, M.; HARÁR, P.
Released
26. 9. 2019
Publisher
MDPI
Location
Switzerland
ISBN
2075-1680
Periodical
Axioms
Year of study
8
Number
4
State
Swiss Confederation
Pages from
1
Pages to
25
Pages count
URL
https://www.mdpi.com/2075-1680/8/4/106
Full text in the Digital Library
http://hdl.handle.net/11012/194791
BibTex
@article{BUT159057, author="Roswitha {Bammer} and Monika {Dörfler} and Pavol {Harár}", title="Gabor frames and deep scattering networks in audio processing", journal="Axioms", year="2019", volume="8", number="4", pages="1--25", doi="10.3390/axioms8040106", issn="2075-1680", url="https://www.mdpi.com/2075-1680/8/4/106" }