Skip navigation
Full metadata record
DC FieldValueLanguage
dc.contributor.authorPerez-Lopez, Andres
dc.contributor.authorFonseca, Eduardo
dc.contributor.authorSerra, Xavier
dc.date.accessioned2019-10-24T01:50:21Z-
dc.date.available2019-10-24T01:50:21Z-
dc.date.issued2019-10
dc.identifier.citationA. Perez-Lopez, E. Fonseca & X. Serra, "A Hybrid Parametric-Deep Learning Approach for Sound Event Localization and Detection", Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019), pages 189–193, New York University, NY, USA, Oct. 2019en
dc.identifier.urihttp://hdl.handle.net/2451/60756-
dc.description.abstractThis work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge. The proposed methodology relies on parametric spatial audio analysis for source localization and detection, combined with a deep learning-based monophonic event classifier. The evaluation of the proposed algorithm yields overall results comparable to the baseline system. The main highlight is a reduction of the localization error on the evaluation dataset by a factor of 2.6, compared with the baseline performance.en
dc.rightsDistributed under the terms of the Creative Commons Attribution 4.0 International (CC-BY) license.en
dc.titleA Hybrid Parametric-Deep Learning Approach for Sound Event Localization and Detectionen
dc.typeArticleen
dc.identifier.DOIhttps://doi.org/10.33682/v1za-0k45
dc.description.firstPage189
dc.description.lastPage193
Appears in Collections:Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)

Files in This Item:
File SizeFormat 
DCASE2019Workshop_Perez-Lopez_58.pdf725 kBAdobe PDFView/Open


Items in FDA are protected by copyright, with all rights reserved, unless otherwise indicated.