6 citations · 18 across the 7 of their papers we have counts for
7 papers
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…
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…
Guided learning for weakly-labeled semi-supervised sound event detection
Liwei Lin, Xiangdong Wang, Hong Liu +1
We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled…
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection
Liwei Lin, Xiangdong Wang, Hong Liu +1
In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are pr…