  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.
