Training a sound event detection algorithm on a heterogeneous dataset including both recorded and synthetic soundscapes that can have various labeling granularity is a non-trivial task that can lead to systems requiring several technical choices. These technical choices are often passed from one system to another without being questioned. We propose to perform a detailed analysis of DCASE 2020 task 4 sound event detection baseline with regards to several aspects such as the type of data used for training, the parameters of the mean-teacher or the transformations applied while generating the synthetic soundscapes. Some of the parameters that are usually used as default are shown to be sub-optimal.

中文翻译：

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在异构数据集上训练声音事件检测

在异构数据集上训练声音事件检测算法，包括可以具有各种标记粒度的录制和合成音景，这是一项非常重要的任务，可能导致系统需要多种技术选择。这些技术选择通常从一个系统传递到另一个系统而不会受到质疑。我们建议从几个方面对 DCASE 2020 任务 4 声音事件检测基线进行详细分析，例如用于训练的数据类型、平均教师的参数或在生成合成音景时应用的变换。通常用作默认值的一些参数显示为次优。