chenchy / accomontage

Codes and MIDI demos for paper: Zhao et al., AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer, ISMIR 2021

AccoMontage

Introduction

AccoMontage is a novel accompaniment arrangement system. It introduces a novel hybrid pathway, in which rule-based optimization and deep learning are both leveraged to complement each other for high-quality generation. Our paper AccoMontage: Accompaniment Arrangement via Phrase Selection and Style Transfer is accepted by ISMIR 2021. This repository stores codes and demos of our work.

Data and Weights Download

Data and weights required to reproduce our work can be downloaded here. The files should be put to ./data files.

For the sake of fast accompaniment arrangement, our model does not run the deep learning inference evrey time, but loads pre-inferenced transition matrix. This pre-inferenced file currently supports transition among 4, 6, and 8-bar phrases. ./util_tools/edge_weights_inference.py can be used to compute transition matrix for other situations.

Run

To inference with AccoMontage and generate for your own music, run AccoMontage_inference.py. Line 197~207 should be configured by yourself.

Demos

Generated demos are listed in ./demo generate upload. The original query lead sheets are listed in demo lead sheets. AccoMontage is applied as one of the backbones to rearrange the university song of East China Normal University (ECNU). Demos are included.

New Feature: Reference Spotlight

This new feature allows you to specify which reference song(s) (from POP909) you would like to use and apply its/their texture to the query lead sheet.

Acknowledgement

Thanks to Prof. Gus Xia, Yixiao Zhang, Liwei Lin, Junyan Jiang, Ziyu Wang, and Shuqi Dai for your generous help to this work. Thanks to all my friends at NYUSH Music X Lab for your encouragement and companion. The following repositories are referred to by this work:

• https://github.com/ZZWaang/polyphonic-chord-texture-disentanglement
• https://github.com/ZZWaang/PianoTree-VAE
• https://github.com/Dsqvival/hierarchical-structure-analysis
• https://github.com/buggyyang/Deep-Music-Analogy-Demos

Contact

For inquries about our work, feel free to contact me at [email protected].

Jingwei Zhao (Ph.D. Student in Data Science, NUS Graduate School)

Aug. 06, 2021