  This report proposes a polyphonic sound event detection (SED) method for the
DCASE 2021 Challenge Task 4. The proposed SED model consists of two stages: a
mean-teacher model for providing target labels regarding weakly labeled or
unlabeled data and a self-training-based noisy student model for predicting
strong labels for sound events. The mean-teacher model, which is based on the
residual convolutional recurrent neural network (RCRNN) for the teacher and
student model, is first trained using all the training data from a weakly
labeled dataset, an unlabeled dataset, and a strongly labeled synthetic
dataset. Then, the trained mean-teacher model predicts the strong label to each
of the weakly labeled and unlabeled datasets, which is brought to the noisy
student model in the second stage of the proposed SED model. Here, the
structure of the noisy student model is identical to the RCRNN-based student
model of the mean-teacher model in the first stage. Then, it is self-trained by
adding feature noises, such as time-frequency shift, mixup, SpecAugment, and
dropout-based model noise. In addition, a semi-supervised loss function is
applied to train the noisy student model, which acts as label noise injection.
The performance of the proposed SED model is evaluated on the validation set of
the DCASE 2021 Challenge Task 4, and then, several ensemble models that combine
five-fold validation models with different hyperparameters of the
semi-supervised loss function are finally selected as our final models.

Sound;Machine Learning;Audio and Speech Processing