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Name: Jingwei Zhao
Type: User
Company: National University of Singapore
Bio: 编程时长两年半的个人程序猿; PhD Student@NUS; Alumni@SJTU&NYUSH.
Location: Singapore
Name: Jingwei Zhao
Type: User
Company: National University of Singapore
Bio: 编程时长两年半的个人程序猿; PhD Student@NUS; Alumni@SJTU&NYUSH.
Location: Singapore
网络系统与控制课程大作业代码
Official PyTorch implementation of "Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Image ", ICCV 2019
Code and MIDI demo for paper: Zhao et al., AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer, ISMIR 2021
Code and demo for paper: Zhao et al., AccoMontage-3: Full-Band Accompaniment Arrangement via Sequential Style Transfer and Multi-Track Function Prior.
Accompaniment Generation via Music Representation Learning
Adversarial EC2-VAE: Melody Generation towards Better Chord Control
Implementing EC2-VAE to the conditional generative model to generate music with controlling rhythm patterns
Codes for ISMIR 2022 paper: Beat Transformer: Demixed Beat and Downbeat Tracking with Dilated Self-Attention
Neural Networks and Deep Learning, NUS CS5242, 2022 Spring
Codes and MIDI demos of ISMIR 2022 paper: Domain Adversarial Training on Conditional Variational Auto-Encoder for Controllable Music Generation
MIDI demos in paper: "DEEP MUSIC ANALOGY VIA LATENT REPRESENTATION DISENTANGLEMENT"
Tensorflow implementation of Learning-based Video Motion Magnification
Algorithm and Data for paper "Automatic Detection of Hierarchical Structure and Influence of Structure on Melody, Harmony and Rhythm in Popular Music"
Python audio and music signal processing library
MuseScore is an open source and free music notation software. For support, contribution, bug reports, visit MuseScore.org. Fork and make pull requests!
The repository of the paper: Wang et al., PIANOTREE VAE: Structured Representation Learning for Polyphonic Music, ISMIR 2020.
The repository of the paper: Wang et al., Learning interpretable representation for controllable polyphonic music generation, ISMIR 2020.
pytorch version of pseudo-3d-residual-networks(P-3D), pretrained model is supported
Pseudo-3D Convolutional Residual Networks for Video Representation Learning
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
Code and demo for paper: Zhao et al., "Q&A: Query-Based Representation Learning for Multi-Track Symbolic Music re-Arrangement," IJCAI 2023
Convert video to 3D pose in one-key.
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