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Yuewei Zhang's Projects

3-ogc icon 3-ogc

Third Open Gravitational-wave Catalog of Compact Binary Mergers. Using the data from LIGO and Virgo from 2015-2019, a comprehensive catalog including 57 detected mergers (55 BBH, 2 BNS) and sub-threshold candidates throughout O1, O2, and O3a.

asg2cap icon asg2cap

Code accompanying the paper "Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs" (Chen et al., CVPR 2020, Oral).

awesome-speech-enhancement icon awesome-speech-enhancement

A tutorial for Speech Enhancement researchers and practitioners. The purpose of this repo is to organize the world’s resources for speech enhancement and make them universally accessible and useful.

bowie icon bowie

BOWIE - Binary Observability With Illustrative Exploration

bsse-se icon bsse-se

Boosting Self-Supervised Embeddings for Speech Enhancement

cmgan icon cmgan

Conformer-based Metric GAN for speech enhancement

cnn_matchfiltering icon cnn_matchfiltering

Pipeline used for classifying BBH, NSBH, and BNS signals in LIGO data. Code used for "Matching Matched Filtering" paper.

cnsurvey icon cnsurvey

一份中文综述文章列表(自然语言处理&机器学习)

cocoapi icon cocoapi

COCO API - Dataset @ http://cocodataset.org/

compas icon compas

COMPAS rapid binary population synthesis code

convwave icon convwave

:ocean: ConvWave :ocean: Searching for Gravitational Waves with Fully Convolutional Neural Nets

d2l-zh icon d2l-zh

《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。

dbt-net icon dbt-net

The audio demos with respect to the paper "DBT-Net: Dual-branch federative magnitude and phase estimation with attention-in-attention transformer for monaural speech enhancement" are provided (submitted to TASLP). The code will also be released soon.

dl-4-tsc icon dl-4-tsc

Deep Learning for Time Series Classification

dpcrn_dns3 icon dpcrn_dns3

Implementation of paper "DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancement"

external-attention-pytorch icon external-attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

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