Title:
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Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks
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Author:
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Fonseca, Eduardo; Gong, Rong; Bogdanov, Dmitry; Slizovskaia, Olga; Gómez Gutiérrez, Emilia, 1975-; Serra, Xavier
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Abstract:
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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. |
Abstract:
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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%. |
Abstract:
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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). |
Subject(s):
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-Acoustic scene classification -Gradient boosting machine -Convolutional neural networks -Ensembling |
Rights:
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This work is licensed under a Creative Commons Attribution 4.0 International License.
http://creativecommons.org/licenses/by/4.0/ |
Document type:
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Conference Object Article - Published version |
Published by:
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Tampere University of Technology
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