PiRhDy: Learning Pitch-, Rhythm-, and Dynamics-aware Embeddings for Symbolic Music (ACM MM 2020 BEST PAPER)
https://dl.acm.org/doi/pdf/10.1145/3394171.3414032 or https://arxiv.org/abs/2010.08091
For citation:
@inproceedings{
liang2020pirhdy,
title={PiRhDy: Learning Pitch-, Rhythm-, and Dynamics-aware Embeddings for Symbolic Music},
author={Liang, Hongru and Lei, Wenqiang and Chan, Paul Yaozhu and Yang, Zhenglu and Sun, Maosong and Chua, Tat-Seng},
booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
pages={574--582},
year={2020}
}
*We suggest you to generate all datasets by yourself, as the datasets are too huge to deliver. *
Any further question, pls email lianghongru@scu.edu.cn (first author) or wenqianglei@scu.edu.com (corresponding author).
step 1: normalize original midi files: time normalization, key tranformation, etc.
step 2: transform midi files into time-pitch matrices
step 3: analysis chord in midi file: not necessary to re-run the files, all needed files already in this dir
step 4: transform matrices into quadruple sequences: (chroma, octave, velocity, state), the final format
step 5:
1) generate datasets for token modeling dataset
2) token modeling
**pre-trained models are in pre-trained-models**
step 6:
1) transform sequence to bars
2) transform bars into phrases
3) generate dataset for context modeling
4) context modeling and downstream tasks
**embeddings pre-trained through token modeling are in "embeddings", models fine-tuned by context modeling are in "pre-trained models".**