Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Beyond DeepJazz

This project focuses on comparison of different generative methods in music generation. We use the exact setting of DeepJazz (preprocessing, grammar, generation pipeline, input / output dimension, certain hyperparameters, etc.) such that a concise and direct of comparison among different models can be observed.

We Acknowledge the great effort by DeepJazz team, it laid a great foundation and interface for our future work. So we can focus on higher-level model selection and comparison.

Structure

/data           # store original training data
/result         # store generation result
/utils          # preprocessing, grammar, helper function
/future         # WIP, future work and adaptation
lstm.py         # models, including LSTM, VAE-LSTM, BI-LSTM
generator.py    # project interface

Reference

Code reference and baseline template

Paper reference in implementation

  • Chen, K., Zhang, W., Dubnov, S., Xia, G., & Li, W. (2019, January). The effect of explicit structure encoding of deep neural networks for symbolic music generation. In 2019 International Workshop on Multilayer Music Representation and Processing (MMRP) (pp. 77-84). IEEE.
    https://arxiv.org/pdf/1811.08380.pdf

  • Performance-RNN by Margenta
    https://magenta.tensorflow.org/performance-rnn

    • Dynamics
    • Temperature and randomness
    • Volumes

Additional Dataset

To download the larger Yamaha e-Piano Competition Dataset, from:

Usage

Use generator.py for public interface

E.g. use lstm model and train 2 epochs

python generator.py --model-choice "lstm" --epochs 2

E.g. use vae-lstm model and train 1 epochs

python generator.py --model-choice "vae-lstm" --epochs 1

E.g. use bi-lstm model and train 128 epochs

python generator.py --model-choice "bi-lstm" --epochs 128

E.g. use bi-lstm model and train 2 epochs, with diversity 0.7

python generator.py --model-choice "bi-lstm" --epochs 2 --diversity 0.7

Requirement

python 2.7
keras
tensorflow
music21
numpy

About

No description, website, or topics provided.

Resources

License

Releases

No releases published

Packages

No packages published

Languages