  Many state-of-the-art systems for audio tagging and sound event detection
employ convolutional recurrent neural architectures. Typically, they are
trained in a mean teacher setting to deal with the heterogeneous annotation of
the available data.
  In this work, we present a thorough analysis of how changing the temporal
resolution of these convolutional recurrent neural networks - which can be done
by simply adapting their pooling operations - impacts their performance. By
using a variety of evaluation metrics, we investigate the effects of adapting
this design parameter under several sound recognition scenarios involving
different needs in terms of temporal localization.
