Abstract: Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application. Index Terms—neural networks, unsupervised learning, semi-supervised learning.

Abstract: The method involves configuring a pretrained text to music AI model that includes a neural network implementing a diffusion model. The process includes receiving audio sample data corresponding to a specific audio concept, generating a concept identifier token based on the audio sample data, adapting a loss function of the diffusion model based on the concept identifier token, selecting pivotal parameters in weight matrices in a self-attention layer of the neural network of the AI model based on the audio sample data, and further training the pivotal parameters of the AI model, to optimize the Al model for the specific audio concept.

Abstract: The present disclosure provides a method for configuring a learning model for music generation and the corresponding learning model. The method includes training a masked autoencoder with training data comprising a combination of a reconstruction loss over time and frequency domains and a patch-based adversarial objective operating at different resolutions. An omnidirectional latent diffusion model is trained based on music data represented in a latent space to obtain a pretrained diffusion model. The pretrained diffusion model is fine-tuned based on text-guided music generation, bidirectional music in-painting, and unidirectional music continuation. The method enables high-fidelity music generation conditioned on text or music representations while maintaining computational efficiency.

Abstract: The present disclosure provides a method for configuring a learning model for music generation and the corresponding learning model. The method includes training a masked autoencoder with training data comprising a combination of a reconstruction loss over time and frequency domains and a patch-based adversarial objective operating at different resolutions. An omnidirectional latent diffusion model is trained based on music data represented in a latent space to obtain a pretrained diffusion model. The pretrained diffusion model is fine-tuned based on text-guided music generation, bidirectional music in-painting, and unidirectional music continuation. The method enables high-fidelity music generation conditioned on text or music representations while maintaining computational efficiency.

Abstract: The present disclosure provides a method for configuring a learning model for music generation and the corresponding learning model. The method includes training a masked autoencoder with training data comprising a combination of a reconstruction loss over time and frequency domains and a patch-based adversarial objective operating at different resolutions. An omnidirectional latent diffusion model is trained based on music data represented in a latent space to obtain a pretrained diffusion model. The pretrained diffusion model is fine-tuned based on text-guided music generation, bidirectional music in-painting, and unidirectional music continuation. The method enables high-fidelity music generation conditioned on text or music representations while maintaining computational efficiency.

Abstract: The method involves configuring a pretrained text to music AI model that includes a neural network implementing a diffusion model. The process includes receiving audio sample data corresponding to a specific audio concept, generating a concept identifier token based on the audio sample data, adapting a loss function of the diffusion model based on the concept identifier token, selecting pivotal parameters in weight matrices in a self-attention layer of the neural network of the AI model based on the audio sample data, and further training the pivotal parameters of the AI model, to optimize the AI model for the specific audio concept.