- Koichi Miyazaki, Tatsuya Komatsu, Tomoki Hayashi, Shinji Watanabe, T. Toda, K. Takeda. 2020. CONVOLUTION-AUGMENTED TRANSFORMER FOR SEMI-SUPERVISED SOUND EVENT DETECTION Technical Report. Abstract: In this technical report, we describe our submission system for DCASE2020 Task4: sound event detection and separation in domestic environments. Our model employs conformer blocks, which combine the self-attention and depth-wise convolution networks, to efﬁciently capture the global and local context information of an audio feature sequence. In addition to this novel architecture, we further improve the performance by utilizing a mean teacher semi-supervised learning technique, data augmentation, and post-processing optimized for each sound event class. We demonstrate that the proposed method achieves the event-based macro F1 score of 50.7% on the validation set, signiﬁcantly outperforming that of the baseline score (34.8%).
