13 citations · 15 across the 9 of their papers we have counts for
9 papers
Speech Enhancement Based on Drifting Models
Liang Xu, Diego Caviedes-Nozal, W. Bastiaan Kleijn +2
We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Rather than relying on iterative…
Data-Free Generalized Zero-Shot Learning
Bowen Tang, Long Yan, Jing Zhang +3
Deep learning models have the ability to extract rich knowledge from large-scale datasets. However, the sharing of data has become increasingly challenging due to concerns regardin…
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…
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…