  In this paper, we propose addressing the lack of strongly labeled data by
using pseudo strongly labeled data approximated using Convolutive Nonnegative
Matrix Factorization. Using this set of data, we then train a novel
architecture called the Convolutional Macaron Net (CMN), which combines
Convolutional Neural Network (CNN) with MN, in a semi-supervised manner.
Instead of training only a single model or using the Mean-teacher approach, we
train two different CMNs synchronously using a curriculum consistency cost and
a curriculum interpolated consistency cost. In the inference stage, one of the
models will provide the frame-level prediction while the other model will
provide the clip-level prediction. Our system outperforms the baseline system
of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020
Challenge Task 4 by a margin of over 10% based on our proposed framework. By
comparing with the top submission of the DCASE 2019 challenge, our system
accuracy is also higher by 1.8%. On the other hand, as compared to the top
submission of DCASE 2020, our accuracy is also marginally higher by 0.3%, even
with fewer Transformer encoding layers. Our system remains robust on unseen
YouTube evaluation dataset and has a winning margin of 0.6% and 6.3% against
the top submission of DCASE 2019 and the baseline system.
