  In this technical report, the systems we submitted for subtask 4 of the DCASE
2021 challenge, regarding sound event detection, are described in detail. These
models are closely related to the baseline provided for this problem, as they
are essentially convolutional recurrent neural networks trained in a mean
teacher setting to deal with the heterogeneous annotation of the supplied data.
However, the time resolution of the predictions was adapted to deal with the
fact that these systems are evaluated using two intersection-based metrics
involving different needs in terms of temporal localization. This was done by
optimizing the pooling operations.
  For the first of the defined evaluation scenarios, imposing relatively strict
requirements on the temporal localization accuracy, our best model achieved a
PSDS score of 0.3609 on the validation data. This is only marginally better
than the performance obtained by the baseline system (0.342): The amount of
pooling in the baseline network already turned out to be optimal, and thus, no
substantial changes were made, explaining this result.
  For the second evaluation scenario, imposing relatively lax restrictions on
the localization accuracy, our best-performing system achieved a PSDS score of
0.7312 on the validation data. This is significantly better than the
performance obtained by the baseline model (0.527), which can effectively be
attributed to the changes that were applied to the pooling operations of the
network.
