Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling
复制标题
将数据增强方案与各种分类器集成以进行声学场景建模
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Yonghong Yan
中科院分区：
文献类型：
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作者：
Hangting Chen;Zuozhen Liu;Zongming Liu;Pengyuan Zhang;Yonghong Yan
本技术报告描述了IOA团队提交的DCASE 2019挑战的TASK 1A。我们的声学场景分类（ASC）系统采用了数据增强方案，采用生成对手网络。在该方案中部署了两个主要的分类器，即集成了尺度图特征的1D深度卷积神经网络和集成了Mel滤波器组特征的2D全卷积神经网络。其他方法，如对手城市适应，基于离散余弦变换的时间模块和混合架构，已被开发用于进一步的融合。我们的实验结果表明，最终的融合系统A-D可以在官方提供的折叠1评估数据集上实现高于85%的准确度。
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.