  Jointly learning from a small labeled set and a larger unlabeled set is an
active research topic under semi-supervised learning (SSL). In this paper, we
propose a novel SSL method based on a two-stage framework for leveraging a
large unlabeled in-domain set. Stage-1 of our proposed framework focuses on
audio-tagging (AT), which assists the sound event detection (SED) system in
Stage-2. The AT system is trained utilizing a strongly labeled set converted
into weak predictions referred to as weakified set, a weakly labeled set, and
an unlabeled set. This AT system then infers on the unlabeled set to generate
reliable pseudo-weak labels, which are used with the strongly and weakly
labeled set to train a frequency dynamic convolutional recurrent neural
network-based SED system at Stage-2 in a supervised manner. Our system
outperforms the baseline by 45.5% in terms of polyphonic sound detection score
on the DESED real validation set.

Audio and Speech Processing