Abstract
The Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge focuses on audio tagging, sound event detection and spatial localisation. DCASE 2019 consists of five tasks: 1) acoustic scene classification, 2) audio tagging with noisy labels and minimal supervision, 3) sound event localisation and detection, 4) sound event detection in domestic environments, and 5) urban sound tagging. In this paper, we propose generic cross-task baseline systems based on convolutional neural networks (CNNs). The motivation is to investigate the performance of a variety of models across several tasks without exploiting the specific characteristics of the tasks. We look at CNNs with 5, 9, and 13 layers, and find that the optimal architecture is task-dependent. For the systems we considered, we found that the 9-layer CNN with average pooling is a good model for a majority of the DCASE 2019 tasks.
Abstract (translated)
2019年声学场景和事件的检测和分类（DCASE）挑战集中在音频标记、声音事件检测和空间定位。DCASE 2019包括五个任务：1）声场分类，2）带噪音标签的音频标签和最低限度的监控，3）声音事件定位和检测，4）家庭环境中的声音事件检测，以及5）城市声音标签。本文提出了一种基于卷积神经网络（CNN）的通用跨任务基线系统。其动机是在不利用任务的特定特性的情况下，研究跨多个任务的各种模型的性能。我们研究了具有5层、9层和13层的CNN，发现最佳架构依赖于任务。对于我们考虑的系统，我们发现具有平均池的9层CNN对于大多数DCASE 2019任务是一个很好的模型。
URL
https://arxiv.org/abs/1904.03476
