Research on Family Activity Recognition Method Based on Additive Margin Capsule Network

被引：0

作者：

Zheng Q.-H. ^{[1
]}

Wang Z.-Q. ^{[1
,2
]}

Liu B.-T. ^{[1
,2
]}

Chen Y. ^{[1
]}

Chen Y.-R. ^{[2
]}

机构：

[1] School of Information Science and Engineering, Changzhou University, Changzhou, 213164, Jiangsu

[2] College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, 310015, Zhejiang

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2020年 / 48卷 / 08期

关键词：

Additive margin softmax; Capsule network; Classification of acoustic events; Family activity recognition;

D O I：

10.3969/j.issn.0372-2112.2020.08.017

中图分类号：

学科分类号：

摘要：

We study the method of family activity recognition based on audio and propose a capsule neural network recognition model based on additive margin.In view of the drawbacks of the traditional capsule neural network objective function only with the output capsule mode length as the constraint, this paper adds a Transition layer to the capsule neural network structure from the perspective of geometry and uses the Transition layer to rebase the capsule unit spatial relationship to the one-dimensional.Then, using the additive margin Softmax as the objective function, the change of similar features is small, and the difference of non-similar features is used as the optimization strategy to construct the objective function based on the capsule vector space relationship to improve model classification ability.Finally, test this method by classified identified for audio events for family activities.Selecting Detection and Classification of Acoustic Scenes and Events(DCASE)2018 Challenge Task 5 as a dataset for classifier construction and testing, with a final average F1 score of 92.3%, which is superior to other mainstream methods. © 2020, Chinese Institute of Electronics. All right reserved.

引用

页码：1580 / 1586

页数：6

共 17 条

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