  Task 4 of the DCASE2018 challenge demonstrated that substantially more
research is needed for a real-world application of sound event detection.
Analyzing the challenge results it can be seen that most successful models are
biased towards predicting long (e.g., over 5s) clips. This work aims to
investigate the performance impact of fixed-sized window median filter
post-processing and advocate the use of double thresholding as a more robust
and predictable post-processing method. Further, four different temporal
subsampling methods within the CRNN framework are proposed: mean-max,
alpha-mean-max, Lp-norm and convolutional. We show that for this task
subsampling the temporal resolution by a neural network enhances the F1 score
as well as its robustness towards short, sporadic sound events. Our best single
model achieves 30.1% F1 on the evaluation set and the best fusion model 32.5%,
while being robust to event length variations.
