Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into pre-defined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment. In this paper, we propose a method for DAC from audio recordings using a multi-scale dilated depthwise separable convolutional network (DSCN). The DSCN is a lightweight neural network with small size of parameters and thus suitable to be deployed in portable terminals with limited computing resources. To expand the receptive field with the same size of DSCN's parameters, dilated convolution, instead of normal convolution, is used in the DSCN for further improving the DSCN's performance. In addition, the embeddings of various scales learned by the dilated DSCN are concatenated as a multi-scale embedding for representing property differences among various classes of domestic activities. Evaluated on a public dataset of the Task 5 of the 2018 challenge on Detection and Classification of Acoustic Scenes and Events (DCASE-2018), the results show that: both dilated convolution and multi-scale embedding contribute to the performance improvement of the proposed method; and the proposed method outperforms the methods based on state-of-the-art lightweight network in terms of classification accuracy.

翻译：音频录音中的日常活动分类旨在将音频片段归入预设的家庭活动类别，是评估居家环境日常活动的有效手段。本文提出一种基于多尺度空洞深度可分离卷积网络的音频日常活动分类方法。该网络作为一种轻量级模型具有参数量小的特点，适合部署于计算资源受限的便携终端。为在保持相同参数规模条件下扩大感受野，本方法采用空洞卷积替代标准卷积以提升网络性能。此外，通过拼接空洞可分离卷积网络学习到的多尺度特征嵌入，构建能表征不同类别活动属性差异的多尺度表示。在2018年声场景与事件检测分类挑战赛（DCASE-2018）任务5公开数据集上的实验结果表明：空洞卷积与多尺度嵌入均有效提升了方法性能；相比基于现有轻量级网络的最优方法，本方法在分类准确率上表现更优。