Classification of one-dimensional non-stationary signals using the Wigner-Ville distribution in convolutional neural networks

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

Abstract

In this paper we argue that the Wigner-Ville distribution (WVD), instead of the spectrogram, should be used as basic input into convolutional neural network (CNN) based classification schemes. The WVD has superior resolution and localization as compared to other time-frequency representations. We present a method where a large-size kernel may be learned from the data, to enhance features important for classification. We back up our claims with theory, as well as application on simulated examples and show superior performance as compared to the commonly used spectrogram.

Detaljer

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Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Signalbehandling
Originalspråkengelska
Titel på värdpublikation25th European Signal Processing Conference, EUSIPCO 2017
FörlagInstitute of Electrical and Electronics Engineers Inc.
Sidor326-330
Antal sidor5
ISBN (elektroniskt)9780992862671
StatusPublished - 2017 okt 23
PublikationskategoriForskning
Peer review utfördJa
Evenemang25th European Signal Processing Conference, EUSIPCO 2017 - Kos island, Kos, Grekland
Varaktighet: 2017 aug 282017 sep 2

Konferens

Konferens25th European Signal Processing Conference, EUSIPCO 2017
LandGrekland
OrtKos
Period2017/08/282017/09/02

Relaterad forskningsoutput

Johan Brynolfsson, 2019 maj 17, Media-Tryck, Lund: Centre for Mathematical Sciences, Lund University. 224 s.

Forskningsoutput: AvhandlingDoktorsavhandling (sammanläggning)

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