Music Phrase Inpainting Using Long-Term Representation and Contrastive Loss
Author:
Affiliation:
1. Fudan University,School of Data Science
2. New York University Shanghai,Music X lab
3. Queen Mary University of London,Centre for Digital Music
4. Fudan University,School of Mathematical Sciences
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747817.pdf?arnumber=9747817
Reference19 articles.
1. Learning interpretable representation for controllable polyphonic music generation;wang;Proceedings of the 21th International Society for Music Information Retrieval Conference ISMIR,2020
2. Transformer VAE: A Hierarchical Model for Structure-Aware and Interpretable Music Representation Learning
3. MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment
4. Learning long-term music representations via hierarchical contextual constraints;wei;Proceedings of the 22nd International Society for Music Information Retrieval Conference ISMIR,2021
5. A simple framework for contrastive learning of visual representations;chen;Proceedings of the ICML International Conference on Machine Learning,2020
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3. Controllable Music Inpainting with Mixed-Level and Disentangled Representation;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04
4. Fine-Tuning Music Generation with Reinforcement Learning Based on Transformer;2022 IEEE 16th International Conference on Anti-counterfeiting, Security, and Identification (ASID);2022-12-02
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