Arranger

Arranger is a project on automatic instrumentation. In a nutshell, we aim to dynamically assign a proper instrument for each note in solo music. Such an automatic instrumentation model could empower a musician to play multiple instruments on a keyboard at the same time. It could also assist a composer in suggesting proper instrumentation for a solo piece.

Our proposed models outperform various baseline models and are able to produce alternative convincing instrumentations for existing arrangements. Check out our demo!

Prerequisites

You can install the dependencies by running pipenv install (recommended) or python3 setup.py install -e .. Python>3.6 is required.

Directory structure

├─ analysis         Notebooks for analysis
├─ scripts          Scripts for running experiments
├─ models           Pretrained models
└─ arranger         Main Python module
   ├─ config.yaml   Configuration file
   ├─ data          Code for collecting and processing data
   ├─ common        Most-common algorithm
   ├─ zone          Zone-based algorithm
   ├─ closest       Closest-pitch algorithm
   ├─ lstm          LSTM model
   └─ transformer   Transformer model

Data preparation

Please follow the instruction in arranger/data/README.md.

Models

LSTM model
- arranger/lstm/train.py: Train the LSTM model
- arranger/lstm/infer.py: Infer with the LSTM model
Transformer model
- arranger/transformer/train.py: Train the Transformer model
- arranger/transformer/infer.py: Infer with the Transformer model

Baseline algorithms

Most-common algorithm
- arranger/common/learn.py: Learn the most common label
- arranger/common/infer.py: Infer with the most-common algorithm
Zone-based algorithm
- arranger/zone/learn.py: Learn the optimal zone setting
- arranger/zone/infer.py: Infer with the zone-based algorithm
Closest-pitch algorithm
- arranger/closest/infer.py: Infer with the closest-pitch algorithm
MLP model
- arranger/mlp/train.py: Train the MLP model
- arranger/mlp/infer.py: Infer with the MLP model

Configuration

In arranger/config.yaml, you can configure the MIDI program numbers used for each track in the sample files generated. You can also configure the color of the generated sample piano roll visualization.

Citing

Please cite the following paper if you use the code provided in this repository.

Hao-Wen Dong, Chris Donahue, Taylor Berg-Kirkpatrick and Julian McAuley, "Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music," Proceedings of the 22nd International Society for Music Information Retrieval Conference (ISMIR), 2021.
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WestCope / arranger