Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling
This is an incremental improvement for acoustic scene modeling, addressing classification accuracy in a specific domain challenge.
The paper tackled acoustic scene classification by integrating a data augmentation scheme using generative adversarial networks with multiple classifiers, achieving over 85% accuracy on the DCASE2019 challenge dataset.
This technical report describes the IOA team's submission for TASK1A of DCASE2019 challenge. Our acoustic scene classification (ASC) system adopts a data augmentation scheme employing generative adversary networks. Two major classifiers, 1D deep convolutional neural network integrated with scalogram features and 2D fully convolutional neural network integrated with Mel filter bank features, are deployed in the scheme. Other approaches, such as adversary city adaptation, temporal module based on discrete cosine transform and hybrid architectures, have been developed for further fusion. The results of our experiments indicates that the final fusion systems A-D could achieve an accuracy higher than 85% on the officially provided fold 1 evaluation dataset.