Sung-Jen Huang


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Lightweight Sound Event Detection Model with RepVGG Architecture
Chia-Chuan Liu | Sung-Jen Huang | Chia-Ping Chen | Chung-Li Lu | Bo-Cheng Chan | Yu-Han Cheng | Hsiang-Feng Chuang | Wei-Yu Chen
Proceedings of the 34th Conference on Computational Linguistics and Speech Processing (ROCLING 2022)

In this paper, we proposed RepVGGRNN, which is a light weight sound event detection model. We use RepVGG convolution blocks in the convolution part to improve performance, and re-parameterize the RepVGG blocks after the model is trained to reduce the parameters of the convolution layers. To further improve the accuracy of the model, we incorporated both the mean teacher method and knowledge distillation to train the lightweight model. The proposed system achieves PSDS (Polyphonic sound event detection score)-scenario 1, 2 of 40.8% and 67.7% outperforms the baseline system of 34.4% and 57.2% on the DCASE 2022 Task4 validation dataset. The quantity of the parameters in the proposed system is about 49.6K, only 44.6% of the baseline system.


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RCRNN-based Sound Event Detection System with Specific Speech Resolution
Sung-Jen Huang | Yih-Wen Wang | Chia-Ping Chen | Chung-Li Lu | Bo-Cheng Chan
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Sound event detection (SED) system outputs sound events and their time boundaries in audio signals. We proposed an RCRNN-based SED system with residual connection and convolution block attention mechanism based on the mean-teacher framework of semi-supervised learning. The neural network can be trained with an amount of weakly labeled data and unlabeled data. In addition, we consider that the speech event has more information than other sound events. Thus, we use the specific time-frequency resolution to extract the acoustic feature of the speech event. Furthermore, we apply data augmentation and post-processing to improve the performance. On the DCASE 2021 Task 4 validation set, the proposed system achieves the PSDS (Poly-phonic Sound Event Detection Score)-scenario 2 of 57.6% and event-based F1-score of 41.6%, outperforming the baseline score of 52.7% and 40.7%.