Coder Social home page Coder Social logo

neuralmelody's Introduction

Overview

  • This repository implements melody generation model proposed in this paper.

  • The input is a two-hot vector in which the first 1 corresponds to a certain chord progression of 2-bar lengths (ex: C - Am), and the second 1 corresponds to the part annotation, e.g., verse, chorus, etc.

  • The output is a MIDI file with generated melody converted from generated strings. Generated strings are currently in the form of pitch;pos;duration.

  • This repository is a modification of NeuralTalk.

Dependencies

Usage

  • To train

    python train.py

  • To deactivate regularization on pitch range

    python train.py --reg_range_coeff 0

  • To set pitch range for regularization (default is 60~72)

    python train.py --reg_range_min your_min_val --reg_range_max your_max_val

  • To generate MIDI file

    python generate_midi.py cv/checkpoint_file

  • To generate MIDI file with HMM-generated input (by default, song will be generated based on our pre-set test input)

    python generate_midi.py cv/checkpoint_file --gen_chords True

  • Notes are inserted to MIDI files on a real-valued time instead of discrete musical lengths, so make sure to quantize it on any sequencer (e.g. GarageBand). 1/16 is recommended.

  • Check our demos

Citation

@article{andrew2017neuralmelody, author={Andrew Shin, Leopold Crestel, Hiroharu Kato, Kuniaki Saito, Katsunori Ohnishi, Masataka Yamaguchi, Masahiro Nakawaki, Yoshitaka Ushiku, Tatsuya Harada}, title={Melody Generation for Pop Music via Word Representation of Musical Properties}, journal={arXiv preprint arXiv:1710.11549}, year={2017} }

License

BSD license.

neuralmelody's People

Contributors

hiroharu-kato avatar shinandrew avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

neuralmelody's Issues

Midi file to Json

hello, i don't konw how to make midi files to one-hot json file ,i learn from your code ,but i don't find the code with process midi files.

How to prepare own dataset

The result of this model sound better than other music generate model. I want to train it by myself data, but I don't know how to prepare the training data. Could you give some introduction.

hmmlearn

I use Python 3.5, but I can‘t install hmmlearn. What's wrong with it?

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.