EasySED: Trusted Sound Event Detection with Self-Distillation


  • Qingsong Zhou Harbin Institute of Technology, Shenzhen, China
  • Kele Xu College of Computer, National University of Defense Technology, Changsha, China National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology, China
  • Ming Feng Tongji University, China




Sound Event Detection, Self-distillation, Machine Listening, Deep Neural Networks


Sound event detection aims to identify the sound events in the audio recordings, whose applications seem to be evident in our daily life, such as the surveillance and monitoring applications. In this paper, we present a novel framework for the detection task, by combining using several improvements. To compress the model efficiently while retaining the detection accuracy, the self-distillation paradigm is employed to improve offline training. To empower the machines with the ability of uncertainty estimation, the Monte Carlo dropout is used in our framework. Moreover, the inference data augmentation strategy is utilized to improve the robustness of the detection task. Lastly, we present an interactive interface, which can be used to visualize the detection and the uncertainty for the prediction. We hope our tool can be helpful for practical machine listening.




How to Cite

Zhou, Q., Xu, K., & Feng, M. (2022). EasySED: Trusted Sound Event Detection with Self-Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13236-13238. https://doi.org/10.1609/aaai.v36i11.21739