2018 SAMGCNNAGatedConvolutionalNeura

• (Shen et al., 2018) ⇒ Yu-Han Shen, Ke-Xin He, and Wei-Qiang Zhang. (2018). “SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring.” In: Proceedings of ISSPIT 2018. arXiv:1810.03986

Subject Headings: Attention Mechanism; Memory Augmented Neural Network System; Convolutional Neural Network

Cited By

• http://scholar.google.com/scholar?q=%222018%22++SAM-GCNN%3A+A+Gated+Convolutional+Neural+Network+with+Segment-Level+Attention+Mechanism+for+Home+Activity+Monitoring

Quotes

Abstract

In this paper, we propose a method for home activity monitoring. We demonstrate our model on dataset of Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 Challenge Task 5. This task aims to classify multi-channel audios into one of the provided pre-defined classes. All of these classes are daily activities performed in a home environment. To tackle this task, we propose a gated convolutional neural network with segment-level attention mechanism (SAM-GCNN). The proposed framework is a convolutional model with two auxiliary modules: a gated convolutional neural network and a segment-level attention mechanism. Furthermore, we adopted model ensemble to enhance the capability of generalization of our model. We evaluated our work on the development dataset of DCASE 2018 Task 5 and achieved competitive performance, with a macro-averaged F-1 score increasing from 83.76% to 89.33%, compared with the convolutional baseline system.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2018 SAMGCNNAGatedConvolutionalNeura	Yu-Han Shen Ke-Xin He Wei-Qiang Zhang			SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring						2018