  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.
