AWS DeepComposer with GANs

 AWS DeepComposer gives you a creative and easy way to get started with machine learning (ML), specifically generative AI. It consists of a USB keyboard that connects to your computer to input melody and the AWS DeepComposer console, which includes AWS DeepComposer Music studio to generate music, learning capsules to dive deep into generative AI models, and AWS DeepComposer Chartbusters challenges to showcase your ML skills.

 

What are GANs?

A GAN is a type of generative machine learning model which pits two neural networks against each other to generate new content: a generator and a discriminator.

• A generator is a neural network that learns to create new data resembling the source data on which it was trained.
• A discriminator is another neural network trained to differentiate between real and synthetic data.

Generator

• The generator takes in a batch of single-track piano rolls (melody) as the input and generates a batch of multi-track piano rolls as the output by adding accompaniments to each of the input music tracks.
• The discriminator then takes these generated music tracks and predicts how far they deviate from the real data present in the training dataset. This deviation is called the generator loss. This feedback from the discriminator is used by the generator to incrementally get better at creating realistic output.

Discriminator

• As the generator gets better at creating music accompaniments, it begins fooling the discriminator. So, the discriminator needs to be retrained as well. The discriminator measures the discriminator loss to evaluate how well it is differentiating between real and fake data. 

• Generator: A neural network that learns to create new data resembling the source data on which it was trained.
• Discriminator: A neural network trained to differentiate between real and synthetic data.
• Generator loss: Measures how far the output data deviates from the real data present in the training dataset.
• Discriminator loss: Evaluates how well the discriminator differentiates between real and fake data.

 

 

The generator and the discriminator are trained in alternating cycles. The generator learns to produce more and more realistic data while the discriminator iteratively gets better at learning to differentiate real data from the newly created data.

Collaboration between an orchestra and its conductor

A simple metaphor of an orchestra and its conductor can be used to understand a GAN. The orchestra trains, practices, and tries to generate polished music, and then the conductor works with them, as both judge and coach. The conductor judges the quality of the output and at the same time provides feedback to achieve a specific style. The more they work together, the better the orchestra can perform.

The GAN models that AWS DeepComposer uses work in a similar fashion. There are two competing networks working together to learn how to generate musical compositions in distinctive styles.

A GAN's generator produces new music as the orchestra does. And the discriminator judges whether the music generator creates is realistic and provides feedback on how to make its data more realistic, just as a conductor provides feedback to make an orchestra sound better.

 

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