• DOI:10.1007/978-3-030-00764-5_2
• Corpus ID: 29157693

Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network

@article{Xu2018MixupBasedAS,
  title={Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network},
  author={Kele Xu and Dawei Feng and Haibo Mi and Boqing Zhu and Dezhi Wang and Lilun Zhang and Hengxing Cai and S. Liu},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.07319}
}

• Kele Xu, Dawei Feng, +5 authors S. Liu
• Published 18 May 2018
• Computer Science
• ArXiv

Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and falls short of accuracy and efficiency. Recently, Convolutional Neural Network (CNN)-based methods have achieved better performance with comparison to the traditional methods. Nevertheless, conventional single channel CNN may fail to consider the fact that additional cues may be embedded in the multi-channel recordings. In this paper, we explore the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner. We conduct the evaluation compared with the conventional CNN and traditional Gaussian Mixture Model-based methods. Moreover, to improve the classification accuracy further, this paper explores the using of mixup method. In brief, mixup trains the neural network on linear combinations of pairs of the representation of audio scene examples and their labels. By employing the mixup approach for data argumentation, the novel model can provide higher prediction accuracy and robustness in contrast with previous models, while the generalization error can also be reduced on the evaluation data.