Music Style Transfer using GANs with Cross-Domain Discriminators - ISMIR 2021

This paper presents a novel approach for music style transfer using Generative Adversarial Networks (GANs) with cross-domain discriminators. The proposed method utilizes a combination of GANs and cross-domain discriminators to effectively transfer musical styles between different domains. This technique addresses challenges in traditional style transfer methods by leveraging the power of GANs for generating realistic and musically coherent output. The paper also discusses the effectiveness of the proposed approach through experimental results and analysis. To learn more about this research, consider exploring academic databases such as Google Scholar or IEEE Xplore using the paper's title or author names. You might also try contacting the authors directly through their websites or institutions for access to the paper or further information.