Jingjing Tang

About Me

I am currently in my final year as a Ph.D. student in Artificial Intelligence and Music (AIM) program, at the C4DM, QMUL, supervised by Prof. George Fazekas and Prof. Geraint Wiggins. My research is centered around applying deep learning techniques to generate expressive piano performances. My academic interests span a range of topics, including expressive music generation, music performance analysis, music information retrieval, and machine learning for music.

Education

• [Sep. 2020 - Now] Ph.D. Student in Artificial Intelligence and Music (AIM) program, at Queen Mary University of London
• [Sep. 2016 - May 2020] Bachelor of Science in Statistics (Data Science Branch), at Chinese Univeristy of Hong Kong, Shenzhen

Research Interests

• Automated Music Generation: controllable music generation, music style transfer, expressive performance rendering and synthesis, music genration evaluation
• Music Information Retrieval: music performer/composer/style classification, music performance analysis, music transcription, music alignment algorithms
• Machine Learning: deep generative models, contrastive learning, transfer learning, language models for music, self-supervised learning

News

• [June. 2025] Our paper “MIDI-VALLE: Improving Expressive Piano Performance Synthesis Through Neural Codec Language Modelling” gets accepted by ISMIR 2026, more details will be announced soon.
• [Apr. 2025] I started my internship at Sony CSL, Japan.
• [Dec. 2024] Our paper “Towards An Integrated Approach for Expressive Piano Performance Synthesis from Music Scores” gets accepted by ICASSP 2025.
• [Nov. 2024] Our paper “EME33: A Dataset of Classical Piano Performances Guided by Expressive Markings with Application in Music Rendering” gets accepted by AIMG 2024 Workshop, at IEEE Big Data 2024.
• [Sep. 2024] I transfer to writing-stage now!
• [Feb. 2024] I started my research internship under supervision of Prof. Yamagishi Junichi at National Institute of Informatics.
• [Nov. 2023] Our papers about reconstructing human expressiveness in piano performances and a dataset of jazz variations for jazz standards were presented in the oral sessions at CMMR 2023, Japan.
• [Oct. 2023] Our paper about a pianist identification system was presented in the poster session at IS2 2023, Italy.
• [Dec. 2022] We released a large-scale dataset of the automatic transcribed expressive piano performances in ISMIR 2022, India.

Publications

Services

Reviewers

• IEEE International Joint Conference on Neural Networks (IJCNN), 2025
• International Society for Music Information Retrieval Conference (ISMIR), 2023-2025
• IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024-2025
• EURASIP Journal on Audio, Speech, and Music Processing

Teaching

• Teaching Fellow: ECS607U - Data Mining 2024-2025 Semester A
• Demonstrator: Research Methods and Responsible Innovation (ECS7007P) 2024-2025 Semester A
• Demonstrator: Research Methods (ECS7029P) 2024-2025 Semester A
• Demonstrator: ECS607U - Data Mining 2022-2023 Semester A
• Demonstrator: ECS766P - Data Mining 2022-2023 Semester A
• Supervisor: EECS MSc Project 2022-23, for 10 students
• Supervisor: EECS MSc Project 2024-25, for 5 students

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