论文标题
Dcase任务4 2022中引入的新颖性的描述和分析在基线系统上
Description and analysis of novelties introduced in DCASE Task 4 2022 on the baseline system
论文作者
论文摘要
声学场景和事件的检测和分类挑战任务4的目的是评估使用异质数据集在家庭环境中检测声音事件的系统。该系统需要能够正确检测录制的音频剪辑中存在的声音事件，并在时间上定位事件。今年的任务是对Dcase 2021任务4的后续工作，并具有一些重要的新颖性。本文的目的是描述和激励这些新增加，并报告其对基线系统的影响的分析。我们介绍了三个主要的新颖性：外部数据集的使用，包括来自Audioset的最近发行的强烈注释的剪辑，利用预训练的模型的可能性以及一个新的能源消耗指标，以提高人们对训练声音事件探测器的生态影响的认识。基线系统上的结果表明，在音频集上预估计的开放源代码在事件分类方面显着改善了结果，但在事件细分方面却不能改善结果。
The aim of the Detection and Classification of Acoustic Scenes and Events Challenge Task 4 is to evaluate systems for the detection of sound events in domestic environments using an heterogeneous dataset. The systems need to be able to correctly detect the sound events present in a recorded audio clip, as well as localize the events in time. This year's task is a follow-up of DCASE 2021 Task 4, with some important novelties. The goal of this paper is to describe and motivate these new additions, and report an analysis of their impact on the baseline system. We introduced three main novelties: the use of external datasets, including recently released strongly annotated clips from Audioset, the possibility of leveraging pre-trained models, and a new energy consumption metric to raise awareness about the ecological impact of training sound events detectors. The results on the baseline system show that leveraging open-source pretrained on AudioSet improves the results significantly in terms of event classification but not in terms of event segmentation.
