GANs Reels

Irish Music

Typically, when someone is talking about Irish music, they mean traditional Irish music, or Irish folk music. Defining Irish music can be difficult because of the vague definition of folk music, and determining the music's origin. Some define traditional music as "old songs, with no known to composers" or as a piece which has accumulated cultural value by being passed down through the generations.

We have three main reasons for choosing to use Irish music:

1. Even with possible cultural influence, Irish music tends to be distinctive with a fairly simple rhythmic structure and a consistent form between pieces.
2. Newfoundland culture has been heavily influenced by Irish immigration, as can be seen in the language and music.
3. An abundance of Irish music exists online already in ABC Notation, which eliminates the need to transcribe music.

For our purposes, we effectively define Irish music as something that the typical person, with little to no musical training, would believe originated in Ireland many years ago, or, a tune that one would expect to be included with other traditional Irish tunes, possibly being played in a pub or a relaxed setting among a group of musicians. In this sense, indistinguishability of the generated tune from other genuine Irish pieces is the key element we're striving for in our output.

• Neural Network

Generative Adversarial Network

An Artificial Neural Network, or ANN, is based on a collection of connected units called nodes, which can be loosely compared to neurons in the human brain. There are various types of ANNs, many of which have been successfully used to classify, predict, and generate different types of data. A Generative Adversarial Network, or a GAN, is an ANN consisting of a Generator that generates data when fed noise, and a Discriminator which classifies data. A common analogy is the Generator is akin to an art forger, while the Discriminator is like a detective. After being trained, the Generator can be used to create original ‘art’.

The GAN has been structured to take advantage of the features that have been observed in the image forms of the Irish tunes in the training dataset. We opted for a Deconvolutional GAN since they are renowned for performing well on images, and making training a lot more stable.

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The discriminator is a 6-tower convolution network. We chose to use dilated filters on 5 of the towers to capture relative information between bars and phrases, with the remaining layer being an undilated 4x4 convolution. A 4x2 filter with a 16x2 dilation captures the $n^{th}$ note of each bar at the first and third or second and fourth phrases, for example. This helps the model learn the structure of music - there are thematic patterns and repetitions at the song level as well as within phrases and within bars. Having multiple towers each learning different aspects gives us a versatility and helps avoid the "tunnel vision" nature of other generative music models.

• Our Approach

Our Approach

We hope to use the distinctive structure of Irish tunes to make them suitable for GAN-based music generation. Our main idea is to regard an Irish melody as a fixed-size object with cross-references among its parts: “music as a picture” view. The two key components of this project are:

1. Encoding temporal information using spatial data.
2. Leveraging knowledge of Irish music to design the model.

• Results

Results

Evaluating the performance of our model is hard since music is subjective. Nevertheless, here are some results. Our code and interesting findings and results will be uploaded here as well as to an online repository for public access here.

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• Listen
• Generate

GAN Generated Music

These songs were generated using our model. You can generate your own unique songs below, using tensorflow js.

T: The Breezeway
C: GANs n Reels
M: 4/4
L: 1/16
K: Dmaj
|B2d2 e2d2 ded2 B2d2|e2d2 d4 f2d2 d4|d2e2 f2d2 e2d2 e2f2|a2g2 f2e2 d4 d4|
|B2d2 e2d2 dedB B2d2|e2d2 d4 f2d2 dBd2|d3e f2d2 e2d2 e2f2|a2g2 f2e2 d4 ded2|
|a4 b2g2 a4 g2a2|b2a2 b2a2 a2fe d2e2|f2a2 a2f2 f2e2 e2g2|a2g2 f2de d4 d2e2|
|a4 abga a4 g2a2|b2ab b2a2 a2fe d2e2|f2a2 a2f2 f2e2 e2g2|a2g2 f2d2 d4 d4|

T: Freshwater Moose
C: GANs n Reels
M: 4/4
L: 1/16
K: Dmaj
|f2e2 e2fe e2d2 d4|ed3 aga2 a4 fgf2|d2e2 d4 d3B e4|f2e2 e2d2 d4 d4|
|fefe e2fe e2d2 ed3|eded gfa2 a4 g2f2|e4 d4 d2Bd e4|f2e2 e2d2 d4 d4|
|e4 e4 fg3 f4|a4 a2g2 e2d2 d4|e4 e4 f2e2 ef2g|a2f2 efd2 d4 d4|
|e4 e3f g4 f4|a4 b2a2 e2d2 d4|e4 e3d d2e2 e2f2|gfe2 f2d2 d4 d4|