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Found 6 papers, 3 papers with code
DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation
Following these guidelines, we propose DSNet, a Dual-Branch CNN architecture, which incorporates atrous convolutions in the shallow layers of the model architecture, as well as pretraining the nearly entire encoder on ImageNet to achieve better performance.
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognition
In this paper, we propose a variance-preserving interpolation framework to improve diffusion models for single-channel speech enhancement (SE) and automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Quality-aware Masked Diffusion Transformer for Enhanced Music Generation
To overcome these challenges, we introduce a novel quality-aware masked diffusion transformer (QA-MDT) approach that enables generative models to discern the quality of input music waveform during training.
Ranked #1 on
Text-to-Music Generation
on MusicCaps
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning
The starting state is noisy speech and the ending state is clean speech.
Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement
The goal of this study is to implement diffusion models for speech enhancement (SE).
VT-CLIP: Enhancing Vision-Language Models with Visual-guided Texts
Contrastive Language-Image Pre-training (CLIP) has drawn increasing attention recently for its transferable visual representation learning.
