Description: Generative Adversarial Networks (GANs) are a type of deep learning architecture used to generate new data from an existing dataset. In the context of music, GANs are applied to create original musical compositions, mimicking styles and structures of pre-existing works. This method is based on the interaction of two neural networks: the generator, which produces new musical samples, and the discriminator, which evaluates the quality of these samples against real ones. Through a competitive training process, where the generator tries to deceive the discriminator, GANs can learn complex patterns and nuances of music, allowing them to generate pieces that can be indistinguishable from those created by humans. This approach opens new creative possibilities for composers and musicians and raises questions about authorship and originality in AI-generated music. GANs for music generation represent a fascinating intersection of technology and art, where innovations in machine learning algorithms translate into new forms of musical expression.
History: Generative Adversarial Networks were introduced by Ian Goodfellow and his colleagues in 2014. Since then, they have rapidly evolved, finding applications in various fields, including music. In the musical realm, researchers began exploring the potential of GANs to generate melodies and harmonies, leading to a growing interest in the intersection of artificial intelligence and musical composition.
Uses: GANs are used in music generation to create original compositions, experiment with new musical styles, and assist composers in the creative process. They are also applied in creating music for video games, movies, and other forms of entertainment, where adaptive or generative music is required.
Examples: A notable example is the project ‘AIVA’ (Artificial Intelligence Virtual Artist), which uses artificial intelligence techniques, including GANs, to compose classical music. Another example is ‘OpenAI Jukedeck’, which allows users to generate personalized music using deep learning algorithms.
