Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks

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• dc.contributor.author Fonseca, Eduardoca
• dc.contributor.author Gong, Rongca
• dc.contributor.author Bogdanov, Dmitryca
• dc.contributor.author Slizovskaia, Olgaca
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-ca
• dc.contributor.author Serra, Xavierca
• dc.date.accessioned 2017-12-11T12:03:58Z
• dc.date.available 2017-12-11T12:03:58Z
• dc.date.issued 2017
• dc.description Comunicació presentada al Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), celebrat el dia 16 de novembre de 2017 a Munic, Alemanya.
• dc.description.abstract This work describes our contribution to the acoustic scene classifi- cation task of the DCASE 2017 challenge. We propose a system that consists of the ensemble of two methods of different nature: a feature engineering approach, where a collection of hand-crafted features is input to a Gradient Boosting Machine, and another approach based on learning representations from data, where log-scaled melspectrograms are input to a Convolutional Neural Network. This CNN is designed with multiple filter shapes in the first layer. We use a simple late fusion strategy to combine both methods. We report classification accuracy of each method alone and the ensemble system on the provided cross-validation setup of TUT Acoustic Scenes 2017 dataset. The proposed system outperforms each of its component methods and improves the provided baseline system by 8.2%.en
• dc.description.sponsorship This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 “AudioCommons”, and the European Research Council under the European Union’s Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).
• dc.format.mimetype application/pdfca
• dc.identifier.citation Fonseca E, Gong R, Bogdanov D, Slizovskaia O, Gomez E, Serra X. Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks. In: Virtanen T, Mesaros A, Heittola T, Diment A, Vincent E, Benetos E, Martinez B, editors. Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017); 2017 Nov 16; Munich, Germany. Tampere (Finland): Tampere University of Technology; 2017. p. 37-41.
• dc.identifier.uri http://hdl.handle.net/10230/33454
• dc.language.iso eng
• dc.publisher Tampere University of Technologyca
• dc.relation.ispartof Virtanen T, Mesaros A, Heittola T, Diment A, Vincent E, Benetos E, Martinez B, editors. Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017); 2017 Nov 16; Munich, Germany. Tampere (Finland): Tampere University of Technology; 2017. p. 37-41.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/688382
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/267583
• dc.rights This work is licensed under a Creative Commons Attribution 4.0 International License.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Acoustic scene classificationen
• dc.subject.keyword Gradient boosting machineen
• dc.subject.keyword Convolutional neural networksen
• dc.subject.keyword Ensemblingen
• dc.title Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networksca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion