  In this paper we present our system for the detection and classification of
acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection
and separation in domestic environments. We introduce two new models: the
forward-backward convolutional recurrent neural network (FBCRNN) and the
tag-conditioned convolutional neural network (CNN). The FBCRNN employs two
recurrent neural network (RNN) classifiers sharing the same CNN for
preprocessing. With one RNN processing a recording in forward direction and the
other in backward direction, the two networks are trained to jointly predict
audio tags, i.e., weak labels, at each time step within a recording, given that
at each time step they have jointly processed the whole recording. The proposed
training encourages the classifiers to tag events as soon as possible.
Therefore, after training, the networks can be applied to shorter audio
segments of, e.g., 200 ms, allowing sound event detection (SED). Further, we
propose a tag-conditioned CNN to complement SED. It is trained to predict
strong labels while using (predicted) tags, i.e., weak labels, as additional
input. For training pseudo strong labels from a FBCRNN ensemble are used. The
presented system scored the fourth and third place in the systems and teams
rankings, respectively. Subsequent improvements allow our system to even
outperform the challenge baseline and winner systems in average by,
respectively, 18.0% and 2.2% event-based F1-score on the validation set. Source
code is publicly available at https://github.com/fgnt/pb_sed.
