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

SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring