MULTI-VIEW INFANT CRY CLASSIFICATION

Yadisbel Martinez Cañete, Hichem Sahli, Abel Diaz Berenguer

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

This paper addresses infant cry classification in multi-view settings, that is, settings where the typical low-level representations, commonly used for audio recognition tasks, are considered as different views of the target data. We show that
through the use of multi-view methods, such as Structured Latent Multi-View Representation Learning, we are able to reliably discriminate between normal and pathological infant cry signals. Extensive experimental results on two benchmark
infant cry data sets indicate that the proposed method outperforms state-of-the-art models.
Original languageEnglish
Title of host publicationSpringer Lecture Notes in Computer Science. Pattern Recognition and Image Analysis.
Subtitle of host publication11th Iberian Conference on Pattern Recognition and Image Analysis Alicante, Spain, Proceedings. June 27-30, 2023
EditorsAntonio Pertusa, Antonio Javier Gallego, Joan Andreu Sánchez, Inês Domingues
PublisherSpringer, Cham
Pages 639–653
Number of pages15
Volume14062
ISBN (Electronic)978-3-031-36616-1
ISBN (Print)978-3-031-36615-4
DOIs
Publication statusPublished - 25 Jun 2023
EventIberian Conference on Pattern Recognition and Image Analysis - Alicante, Spain
Duration: 27 Jun 202330 Jun 2023
Conference number: 11th
http://www.ibpria.org/2023/

Conference

ConferenceIberian Conference on Pattern Recognition and Image Analysis
Abbreviated titleIbPRIA 2023
Country/TerritorySpain
CityAlicante
Period27/06/2330/06/23
Internet address

Keywords

  • Classification
  • Infant cry
  • Multi-view

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