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20182023
most citedAn End-to-end Approach for Lexical Stress Detection based on Transformer

3 citations · 5 across the 6 of their papers we have counts for

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eess.AS2023

Audio-free Prompt Tuning for Language-Audio Models

Yiming Li, Xiangdong Wang, Hong Liu

Contrastive Language-Audio Pretraining (CLAP) is pre-trained to associate audio features with human language, making it a natural zero-shot classifier to recognize unseen sound cat…

eess.AS2023

Semi-supervised Sound Event Detection with Local and Global Consistency Regularization

Yiming Li, Xiangdong Wang, Hong Liu +3

Learning meaningful frame-wise features on a partially labeled dataset is crucial to semi-supervised sound event detection. Prior works either maintain consistency on frame-level p…

eess.AS2020

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…

eess.AS20193 cited

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…

eess.AS2019

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…