3 citations · 5 across the 4 of their papers we have counts for
10 papers
A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4
Yiming Li, Zhifang Guo, Zhirong Ye +6
In this paper, we describe in detail our system for DCASE 2022 Task4. The system combines two considerably different models: an end-to-end Sound Event Detection Transformer (SEDT)…
Learning generic feature representation with synthetic data for weakly-supervised sound event detection by inter-frame distance loss
Yuxin Huang, Liwei Lin, Xiangdong Wang +4
Due to the limitation of strong-labeled sound event detection data set, using synthetic data to improve the sound event detection system performance has been a new research focus.…
Guided multi-branch learning systems for sound event detection with sound separation
Yuxin Huang, Liwei Lin, Shuo Ma +5
In this paper, we describe in detail our systems for DCASE 2020 Task 4. The systems are based on the 1st-place system of DCASE 2019 Task 4, which adopts weakly-supervised framework…
Multi-Branch Learning for Weakly-Labeled Sound Event Detection
Yuxin Huang, Xiangdong Wang, Liwei Lin +2
There are two sub-tasks implied in the weakly-supervised SED: audio tagging and event boundary detection. Current methods which combine multi-task learning with SED requires annota…
An End-to-end Approach for Lexical Stress Detection based on Transformer
Yong Ruan, Xiangdong Wang, Hong Liu +4
The dominant automatic lexical stress detection method is to split the utterance into syllable segments using phoneme sequence and their time-aligned boundaries. Then we extract fe…
Guided Learning Convolution System for DCASE 2019 Task 4
Liwei Lin, Xiangdong Wang, Hong Liu +1
In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments. We employ a convolutional neural network (CN…