vjmedia - User contributions [en]

DeepHarmony

2016-10-12T00:05:00Z

Skhalandovsky: /* Recommended skills: = */

#PICTURE HERE#

=Summary=

Deep Harmony is a machine learning project for creating harmonies based on a given melody.

Currently, Deep Harmony is learning to mimic the behavior of Bach Chorales created by [http://artsites.ucsc.edu/faculty/cope/ David Cope].

Deep Harmony is written in [https://www.python.org Python 3.5] and uses the [https://keras.io Keras] machine learning library.

=Possible Tasks=

==Change out learning dataset==
Deep Harmony currently tries to emulate David Cope's machine generated 5000 Bach Chorales. You could change the style of music it learns from.
===== Recommended skills: =====
* Some Python programming
* It would be nice to have some familiarity with machine learning before hand

==Improving Deep Harmony's note prediction capabilities==
Deep Harmony's neural network is fairly basic and not capable of predicting many of the complexities of music. You could improve the performance of Deep Harmony by changing the structure and layers used in the deep neural network.
===== Recommended skills:=====
* Basic Python programming
* Some familiarity with machine learning

== Make Deep Harmony predict whole songs ==
Deep Harmony currently creates the next note corresponding to a melody line, this is great for generating accompaniment, but you could make it generate entire songs.
==== Recommended skills: ====
* Basic Python programming
* Familiarity with machine learning

==Integrating Deep Harmony into a Max Patch==
Deep Harmony could be used to create real-time music in Max/MSP. Other people have already created ways to run Python code in Max/MSP [http://grrrr.org/research/software/py/ pyext (not actively maintained)] [https://cycling74.com/toolbox/python/#.V_15NjK-JE4 Embedding Jython in Max (probably not what we want)]
===== Recommended skills: =====
* Basic Python programming
* Familiarity with Max/MSP
* Patience for figuring out installation/configuration issues

=Getting Deep Harmony set up on your computer=
All project files and resources are located in this git repository:
[https://github.com/samkhal/deepharmony https://github.com/samkhal/deepharmony]

It can be downloaded as a .zip file or cloned as a repository.

==Dependencies:==
* [http://jupyter.org Jupyter Notebook], an interactive programming environment and editor (necessary for using the .ipynb files)
* Python 3.5, and the following libraries:
** numpy and matplotlib, for numerical computation and graphical plotting.
** [http://web.mit.edu/music21 music21], for processing music data
** [https://pypi.python.org/pypi/tqdm tqdm], for nice progress bars
** [https://keras.io Keras], a deep learning library. We recommend installing it with the Theano backend, but the code should work with the TensorFlow backend as well.
* musescore 2+, for allowing music21 to render music as pictures

Jupyter Notebook, Python, Numpy, and Matplotlib are all packaged together in [https://www.continuum.io/downloads Anaconda], and we recommend installing them that way.

=WPI Student contributors=
==2016==
Sam Khalandovsky

Ezra Davis

DeepHarmony

2016-10-12T00:04:42Z

Skhalandovsky: /* Dependencies: */

#PICTURE HERE#

=Summary=

Deep Harmony is a machine learning project for creating harmonies based on a given melody.

Currently, Deep Harmony is learning to mimic the behavior of Bach Chorales created by [http://artsites.ucsc.edu/faculty/cope/ David Cope].

Deep Harmony is written in [https://www.python.org Python 3.5] and uses the [https://keras.io Keras] machine learning library.

=Possible Tasks=

==Change out learning dataset==
Deep Harmony currently tries to emulate David Cope's machine generated 5000 Bach Chorales. You could change the style of music it learns from.
===== Recommended skills: =====
* Some Python programming
* It would be nice to have some familiarity with machine learning before hand

==Improving Deep Harmony's note prediction capabilities==
Deep Harmony's neural network is fairly basic and not capable of predicting many of the complexities of music. You could improve the performance of Deep Harmony by changing the structure and layers used in the deep neural network.
===== Recommended skills:=====
* Basic Python programming
* Some familiarity with machine learning

== Make Deep Harmony predict whole songs ==
Deep Harmony currently creates the next note corresponding to a melody line, this is great for generating accompaniment, but you could make it generate entire songs.
==== Recommended skills: =====
* Basic Python programming
* Familiarity with machine learning

==Integrating Deep Harmony into a Max Patch==
Deep Harmony could be used to create real-time music in Max/MSP. Other people have already created ways to run Python code in Max/MSP [http://grrrr.org/research/software/py/ pyext (not actively maintained)] [https://cycling74.com/toolbox/python/#.V_15NjK-JE4 Embedding Jython in Max (probably not what we want)]
===== Recommended skills: =====
* Basic Python programming
* Familiarity with Max/MSP
* Patience for figuring out installation/configuration issues

=Getting Deep Harmony set up on your computer=
All project files and resources are located in this git repository:
[https://github.com/samkhal/deepharmony https://github.com/samkhal/deepharmony]

It can be downloaded as a .zip file or cloned as a repository.

==Dependencies:==
* [http://jupyter.org Jupyter Notebook], an interactive programming environment and editor (necessary for using the .ipynb files)
* Python 3.5, and the following libraries:
** numpy and matplotlib, for numerical computation and graphical plotting.
** [http://web.mit.edu/music21 music21], for processing music data
** [https://pypi.python.org/pypi/tqdm tqdm], for nice progress bars
** [https://keras.io Keras], a deep learning library. We recommend installing it with the Theano backend, but the code should work with the TensorFlow backend as well.
* musescore 2+, for allowing music21 to render music as pictures

Jupyter Notebook, Python, Numpy, and Matplotlib are all packaged together in [https://www.continuum.io/downloads Anaconda], and we recommend installing them that way.

=WPI Student contributors=
==2016==
Sam Khalandovsky

Ezra Davis

DeepHarmony

2016-10-11T23:59:03Z

Skhalandovsky: /* Dependencies: */

Music from Cellular Automata

2015-12-18T05:18:32Z

Skhalandovsky:

The project involved exploring cellular automata as a way of procedurally generating music. By creating a cellular automaton in Max and developing an interesting mapping of the automaton's state to musical sounds, a piece of music can be created based on very simple starting rules. The "composer" of a piece would then input a series of starting states, from which the automaton would evolve on its own. I created a patch that allows the user to pick on of the 256 elementary cellular automata rules and evolved it, while mapping cells to notes to produce an interesting musical composition. The notefilter object, which I use to set up this mapping, can be used to map any number of instruments to any regions of the automata, while being able to select the specify the scale, timbre, and other parameters.

The Max patch can be downloaded here [http://sam.khalandovsky.com/misc/CellularAutomaton.zip here].

----
<mediaplayer>http://sam.khalandovsky.com/misc/FinalRecording.mp4</mediaplayer>

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions‏‎ ]] [[Category:Advisor:Manzo]]

Music from Cellular Automata

2015-12-16T16:07:01Z

Skhalandovsky:

Creating Music from Cellular Automata

----

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions‏‎ ]]

Music from Cellular Automata

2015-12-16T16:06:26Z

Skhalandovsky: Created page with "Creating Music from Cellular Automata ---- Category:Algorithmic Composition"

Creating Music from Cellular Automata

----

[[Category:Algorithmic Composition]]

Sam Khalandovsky- Game of Thrones in Max

2015-03-18T17:16:49Z

Skhalandovsky:

This is my final project for MU 230X. In Max/MSP, I created a patch to generate music with an element of randomness based on phrases from the Game of Thrones theme.

http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/FinalProject.zip

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions]]
[[Category: Advisor:Manzo]]

Sam Khalandovsky- Game of Thrones in Max

2014-05-05T00:55:50Z

Skhalandovsky:

This is my final project for MU 230X. In Max/MSP, I created a patch to generate music with an element of randomness based on phrases from the Game of Thrones theme.

http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/FinalProject.zip

<mediaplayer>http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/MusicFinalVid.mp4</mediaplayer>

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions]]
[[Category: Advisor:Manzo]]

Sam Khalandovsky- Game of Thrones in Max

2014-05-05T00:37:40Z

Skhalandovsky:

This is my final project for MU 230X. In Max/MSP, I created a patch to generate music with an element of randomness based on phrases from the Game of Thrones theme.

http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/FinalProject.zip

[[File:http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/FinalProject.zip]]

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions]]
[[Category: Advisor:Manzo]]

Sam Khalandovsky- Game of Thrones in Max

2014-05-05T00:37:05Z

Skhalandovsky:

This is my final project for MU 230X. In Max/MSP, I created a patch to generate music with an element of randomness based on phrases from the Game of Thrones theme.

[http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/FinalProject.zip]

[[File:http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/FinalProject.zip]]

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions]]
[[Category: Advisor:Manzo]]

Sam Khalandovsky- Game of Thrones in Max

2014-05-05T00:30:27Z

Skhalandovsky:

This is my final project for MU 230X. In Max/MSP, I created a patch to generate music with an element of randomness based on phrases from the Game of Thrones theme.

[[File:http://media.wpi.edu/Academics/Depts/HUA/Manzo/New%20Folder%20%2811%29/FinalProject.zip]]

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions]]
[[Category: Advisor:Manzo]]

Sam Khalandovsky- Game of Thrones in Max

2014-05-05T00:28:08Z

Skhalandovsky: Created page with 'This is my final project for MU 230X. In Max/MSP, I created a patch to generate music with an element of randomness based on phrases from the Game of Thrones theme. [[Category:A…'

This is my final project for MU 230X. In Max/MSP, I created a patch to generate music with an element of randomness based on phrases from the Game of Thrones theme.

[[Category:Algorithmic, Interactive, & Electro-acoustic Compositions]]
[[Category: Advisor:Manzo]]