Search Results for author: Donald S. Williamson

Found 6 papers, 0 papers with code

MMViT: Multiscale Multiview Vision Transformers

no code implementations28 Apr 2023 Yuchen Liu, Natasha Ong, Kaiyan Peng, Bo Xiong, Qifan Wang, Rui Hou, Madian Khabsa, Kaiyue Yang, David Liu, Donald S. Williamson, Hanchao Yu

Our model encodes different views of the input signal and builds several channel-resolution feature stages to process the multiple views of the input at different resolutions in parallel.

Image Classification

Attention-based Speech Enhancement Using Human Quality Perception Modelling

no code implementations23 Mar 2023 Khandokar Md. Nayem, Donald S. Williamson

In this work, we propose an attention-based enhancement approach that uses learned speech embedding vectors from a mean-opinion score (MOS) prediction model and a speech enhancement module to jointly enhance noisy speech.

Language Modelling Speech Enhancement

A Composite T60 Regression and Classification Approach for Speech Dereverberation

no code implementations9 Feb 2023 Yuying Li, Yuchen Liu, Donald S. Williamson

More specifically, we develop a joint learning approach that uses a composite T60 module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation.

regression Speech Dereverberation

Multi-channel Multi-frame ADL-MVDR for Target Speech Separation

no code implementations24 Dec 2020 Zhuohuang Zhang, Yong Xu, Meng Yu, Shi-Xiong Zhang, LianWu Chen, Donald S. Williamson, Dong Yu

Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are harmful to modern automatic speech recognition (ASR) systems.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

A Pyramid Recurrent Network for Predicting Crowdsourced Speech-Quality Ratings of Real-World Signals

no code implementations31 Jul 2020 Xuan Dong, Donald S. Williamson

The real-world capabilities of objective speech quality measures are limited since current measures (1) are developed from simulated data that does not adequately model real environments; or they (2) predict objective scores that are not always strongly correlated with subjective ratings.

On Loss Functions and Recurrency Training for GAN-based Speech Enhancement Systems

no code implementations29 Jul 2020 Zhuohuang Zhang, Chengyun Deng, Yi Shen, Donald S. Williamson, Yongtao Sha, Yi Zhang, Hui Song, Xiangang Li

Recent work has shown that it is feasible to use generative adversarial networks (GANs) for speech enhancement, however, these approaches have not been compared to state-of-the-art (SOTA) non GAN-based approaches.

Audio and Speech Processing Sound

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