Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks
Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks
Citació
- 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.
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Descripció
Resum
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%.Descripció
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.