基于多尺度扩张深度可分离卷积网络的音频录音家庭活动分类
Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network
AI总结：
针对家庭音频活动分类任务，提出多尺度扩张深度可分离卷积网络，通过扩张卷积扩感受野、多尺度嵌入表征类别差异，在DCASE-2018任务5数据集上精度优于现有轻量级网络方法。
摘要：
基于音频录音的家庭活动分类（Domestic Activities Classification，DAC）旨在将音频录音分类为预定义的家庭活动类别，是评估家庭环境中日常活动的有效方法。本文提出一种基于多尺度扩张深度可分离卷积网络（DSCN）的音频DAC方法。DSCN是一种轻量级神经网络，参数量小，适合部署在计算资源有限的便携式终端中。为在DSCN参数量不变的情况下扩大感受野，该网络采用扩张卷积替代普通卷积，以进一步提升DSCN的性能。此外，将扩张DSCN学习到的不同尺度嵌入向量拼接为多尺度嵌入，以表征不同类别家庭活动之间的属性差异。在2018年声学场景与事件检测与分类挑战赛（DCASE-2018）任务5的公开数据集上的评估结果表明：扩张卷积和多尺度嵌入均有助于提升所提方法的性能；且所提方法在分类精度上优于基于现有最先进轻量级网络的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
