Ridge curves retrieval in time–frequency (TF) domains is fundamental in many application fields as they convey most of information concerning the instantaneous frequencies of non-stationary signals. However, it represents a very hard task in the case of multicomponent signals having non-separable modes as they generate interference in TF domains. A preliminary detection of these interference regions may be then useful for the definition of effective strategies for ridge curve recovery. This paper introduces SIID-CNN (Spectrogram Image Interference Detection via CNN), that is a novel approach based on machine learning for the automatic detection of interference regions in spectrogram images. Each spectrogram sample is suitably classified as interference, single mode or background by accounting for its relative information. Some critical problems, such as the training set size and the type of examples to use for populating the training set, are dealt with. Experimental results show that a local linear model for spectrogram image and a small training set can guarantee good classification rates for a wide class of non-stationary signals, even in the presence of moderate noise.

A supervised approach for the detection of AM-FM signals’ interference regions in spectrogram images / Bruni, V.; Vitulano, D.; Marconi, S.. - In: IMAGE AND VISION COMPUTING. - ISSN 0262-8856. - 138:(2023). [10.1016/j.imavis.2023.104812]

A supervised approach for the detection of AM-FM signals’ interference regions in spectrogram images

Bruni V.
;
Vitulano D.;Marconi S.
2023

Abstract

Ridge curves retrieval in time–frequency (TF) domains is fundamental in many application fields as they convey most of information concerning the instantaneous frequencies of non-stationary signals. However, it represents a very hard task in the case of multicomponent signals having non-separable modes as they generate interference in TF domains. A preliminary detection of these interference regions may be then useful for the definition of effective strategies for ridge curve recovery. This paper introduces SIID-CNN (Spectrogram Image Interference Detection via CNN), that is a novel approach based on machine learning for the automatic detection of interference regions in spectrogram images. Each spectrogram sample is suitably classified as interference, single mode or background by accounting for its relative information. Some critical problems, such as the training set size and the type of examples to use for populating the training set, are dealt with. Experimental results show that a local linear model for spectrogram image and a small training set can guarantee good classification rates for a wide class of non-stationary signals, even in the presence of moderate noise.
2023
Convolutional neural networks; Instantaneous frequency estimation; Interfering AM-FM signals; Machine learning; Multicomponent signals; Spectrogram classification; Supervised learning
01 Pubblicazione su rivista::01a Articolo in rivista
A supervised approach for the detection of AM-FM signals’ interference regions in spectrogram images / Bruni, V.; Vitulano, D.; Marconi, S.. - In: IMAGE AND VISION COMPUTING. - ISSN 0262-8856. - 138:(2023). [10.1016/j.imavis.2023.104812]
File allegati a questo prodotto
File Dimensione Formato  
SIVIAI_VBv3.pdf

accesso aperto

Tipologia: Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 6.05 MB
Formato Adobe PDF
6.05 MB Adobe PDF
Bruni_supervised-approach_2023.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.96 MB
Formato Adobe PDF
4.96 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1689788
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact