<p>There are a few of them. The most recent I've found is from DeepMind: <a href="https://arxiv.org/abs/1806.10474" rel="nofollow noreferrer">The challenge of realistic music generation: modelling raw audio at scale</a>. This <a href="https://www.youtube.com/watch?v=8GUYAVXmhsI" rel="nofollow noreferrer">video</a> is a great analysis of it.</p>

CC BY-SA 4.0
2018-08-30T11:36:50.247Z
I like this answer, but maybe you could link to some other ones. EvoMusArt puts out a few papers every year, for example: http://www.evostar.org/2018/cfp_evomusart.php#abstracts.
John Doucette
CC BY-SA 4.0
2018-08-30T12:14:55.060Z
@JohnDoucette I don't know much on the theme, and I didn't know that article/team. You should compile your own answer, so the OP gets notified
BlueMoon93
CC BY-SA 4.0
2018-08-27T16:17:06.133Z
2021-01-20T22:01:11.443Z
2021-01-20T22:01:11.443Z
BlueMoon93
<p>I am also new to the neural network architecture game but from what I have learned so far I think you have a few good options to choose from. </p>

<p>A recurrent neural network (RNN) would be a standard approach but if you're looking for something more robust you could look into a <a href="https://en.wikipedia.org/wiki/Long_short-term_memory" rel="nofollow noreferrer">Long Short Term Memory network</a> (LSTM). The neurons have a memory of past events and can recall that later on. It is a subset of RNN.</p>

<p>Perhaps you could go a little further and use a <a href="https://en.wikipedia.org/wiki/Convolutional_neural_network" rel="nofollow noreferrer">Convolutional Neural Network</a> (CNN). So far these type of networks have been highly successful for image recognition. You could abstract a song piece as an image. Each pixel could be a progression in time and the value of the pixel could be the actual note.</p>

<p>Also take a look at <a href="https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464" rel="nofollow noreferrer">this article</a> for a good overview of several different neural network types.</p>

CC BY-SA 4.0
2018-08-30T14:29:15.733Z
2018-08-30T14:29:15.733Z
Jeff Wingo
<p>What are the best machine learning models that have been used to compose music? Are there some good research papers (or books) on this topic out there?</p>
<p>I would say, if I use a neural network, I would opt for a recurrent one, because it needs to have a concept of timing, chord progressions, and so on.</p>
<p>I am also wondering how the loss function would look like, and how I could give the AI so much feedback as they usually need.</p>

<neural-networks><machine-learning><recurrent-neural-networks><reference-request><generative-model>
What are the best machine learning models for music composition?
Ben
# What are the best machine learning models for music composition?

(Asked by: username_0 on 2018-08-27)

What are the best machine learning models that have been used to compose music? Are there some good research papers (or books) on this topic out there?

I would say, if I use a neural network, I would opt for a recurrent one, because it needs to have a concept of timing, chord progressions, and so on.

I am also wondering how the loss function would look like, and how I could give the AI so much feedback as they usually need.

**Tags:** neural-networks, machine-learning, recurrent-neural-networks, reference-request, generative-model


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## 2 answers

### Answer by username_1 on 2018-08-27. Score: 1

There are a few of them. The most recent I've found is from DeepMind: [The challenge of realistic music generation: modelling raw audio at scale](https://arxiv.org/abs/1806.10474). This [video](https://www.youtube.com/watch?v=8GUYAVXmhsI) is a great analysis of it.

> **Comments:**
> **username_2**: I like this answer, but maybe you could link to some other ones. EvoMusArt puts out a few papers every year, for example: http://www.evostar.org/2018/cfp_evomusart.php#abstracts.  
> **username_1**: @JohnDoucette I don't know much on the theme, and I didn't know that article/team. You should compile your own answer, so the OP gets notified  

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### Answer by username_3 on 2018-08-30. Score: 0

I am also new to the neural network architecture game but from what I have learned so far I think you have a few good options to choose from.

A recurrent neural network (RNN) would be a standard approach but if you're looking for something more robust you could look into a [Long Short Term Memory network](https://en.wikipedia.org/wiki/Long_short-term_memory) (LSTM). The neurons have a memory of past events and can recall that later on. It is a subset of RNN.

Perhaps you could go a little further and use a [Convolutional Neural Network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN). So far these type of networks have been highly successful for image recognition. You could abstract a song piece as an image. Each pixel could be a progression in time and the value of the pixel could be the actual note.

Also take a look at [this article](https://towardsdatascience.com/the-mostly-complete-chart-of-neural-networks-explained-3fb6f2367464) for a good overview of several different neural network types.


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