  This report proposes a polyphonic sound event detection (SED) method for the
DCASE 2020 Challenge Task 4. The proposed SED method is based on
semi-supervised learning to deal with the different combination of training
datasets such as weakly labeled dataset, unlabeled dataset, and strongly
labeled synthetic dataset. Especially, the target label of each audio clip from
weakly labeled or unlabeled dataset is first predicted by using the mean
teacher model that is the DCASE 2020 baseline. The data with predicted labels
are used for training the proposed SED model, which consists of CNNs with skip
connections and self-attention mechanism, followed by RNNs. In order to
compensate for the erroneous prediction of weakly labeled and unlabeled data, a
semi-supervised loss function is employed for the proposed SED model. In this
work, several versions of the proposed SED model are implemented and evaluated
on the validation set according to the different parameter setting for the
semi-supervised loss function, and then an ensemble model that combines
five-fold validation models is finally selected as our final model.
