Fonseca, E., Gong R., Bogdanov D., Slizovskaia O., Gomez E., Serra X. Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks. Workshop on Detection and Classification of Acoustic Scenes and Events
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Fonseca, E., Gong R., Bogdanov D., Slizovskaia O., Gomez E., Serra X. Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks. Workshop on Detection and Classification of Acoustic Scenes and Events
Fonseca, E., Gong R., Bogdanov D., Slizovskaia O., Gomez E., Serra X. Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks. Workshop on Detection and Classification of Acoustic Scenes and Events
This work describes our contribution to the acoustic scene classification 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 mel-spectrograms 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%.
Additional material:
- Support repository https://edufonseca.github.io/DCASE2017-Task1-ASC/
- Post-print available at UPF e-repository