13 citations · 36 across the 18 of their papers we have counts for
18 papers
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…
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…
Frame Pairwise Distance Loss for Weakly-supervised Sound Event Detection
Rui Tao, Yuxing Huang, Xiangdong Wang +4
Weakly-supervised learning has emerged as a promising approach to leverage limited labeled data in various domains by bridging the gap between fully supervised methods and unsuperv…
Audio Generation with Multiple Conditional Diffusion Model
Zhifang Guo, Jianguo Mao, Rui Tao +4
Text-based audio generation models have limitations as they cannot encompass all the information in audio, leading to restricted controllability when relying solely on text. To add…
Leveraging Language Model Capabilities for Sound Event Detection
Hualei Wang, Jianguo Mao, Zhifang Guo +3
Large language models reveal deep comprehension and fluent generation in the field of multi-modality. Although significant advancements have been achieved in audio multi-modality,…
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)…
Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection
Zhirong Ye, Xiangdong Wang, Hong Liu +4
Sound event detection (SED) has gained increasing attention with its wide application in surveillance, video indexing, etc. Existing models in SED mainly generate frame-level predi…
SP-SEDT: Self-supervised Pre-training for Sound Event Detection Transformer
Zhirong Ye, Xiangdong Wang, Hong Liu +4
Recently, an event-based end-to-end model (SEDT) has been proposed for sound event detection (SED) and achieves competitive performance. However, compared with the frame-based mode…
Couple Learning for semi-supervised sound event detection
Rui Tao, Long Yan, Kazushige Ouchi +1
The recently proposed Mean Teacher method, which exploits large-scale unlabeled data in a self-ensembling manner, has achieved state-of-the-art results in several semi-supervised l…
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…
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…
DSBI: Double-Sided Braille Image Dataset and Algorithm Evaluation for Braille Dots Detection
Renqiang Li, Hong Liu, Xiangdong Wan +1
Braille is an effective way for the visually impaired to learn knowledge and obtain information. Braille image recognition aims to automatically detect Braille dots in the whole Br…
Estimation of Spatial-Temporal Gait Parameters based on the Fusion of Inertial and Film-Pressure Signals
Cheng Wang, Xiangdong Wang, Zhou Long +5
Gait analysis (GA) has been widely used in physical activity monitoring and clinical contexts, and the estimation of the spatial-temporal gait parameters is of primary importance f…