基于注意力胶囊网络的家庭活动识别

Domestic Activity Recognition Based on Attention Capsule Network

本文提出了一种注意力胶囊网络的新框架利用录音识别家庭活动.胶囊网络可以通过动态路由算法来选择基于每个声音事件的代表性频带.为了进一步提高其能力,我们在胶囊网络中加入注意力机制,它通过加权来增加对重要时间帧的关注.为了评估我们的方法,我们在声学场景和事件的检测和分类(Detection and Classification of Acoustic Scenes and Events, DCASE)2018挑战任务5数据集上进行测试.结果表明, F1平均得分可达92.1%,优于几个基线方法的F1得分.

In this paper, a novel framework of attention capsule network is proposed, which uses sound recordings to identify domestic activities. The capsule network can select a representative frequency band based on each sound event by the dynamic routing algorithm. To further improve its ability, we add attention mechanism to the capsule network. It can increase the focus on significant time frames by weighting. To evaluate our approach, we test it on the dataset of task 5 of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2018 Challenge. The results show that the average F1 score can reach92.1 %, outperforming several baselines.