  In Sound Event Detection (SED) systems, the lengths of median filters for
post-processing have never been optimized during training due to several
problems. No gradient is received by the lengths so they cannot be learned
during back-propagation. The median-filtering inserted in the models also
causes block in gradient flowing and the smoothing process misleads the model
by ignoring errors. To resolve these problems, we provide different channels of
features smoothed to different extents along with the original feature, so the
model can optimize the weights while cognizing all the errors. We then use a
linear layer to integrate the results and produce a linear combination. We
further design the soft-median function to dredge the gradient flow. The
proposed framework is called Soft-Median Choice (SMC). Experiments show that
the SMC block not only automatically smooths the features based on the training
set, but also forces the model to extract common features shared by all the
frames of a sound event. The performance of the proposed method outperforms the
baseline by over 10% of Event-Based F1 Score (EBFS) in both the validation and
the evaluation set, and also slightly outperforms the single model of the
state-of-the-art SED system.
