Time-Varying Audio Effect Modeling by End-to-End Adversarial Training
NeutralArtificial Intelligence
- A new paper presents a Generative Adversarial Network (GAN) framework for modeling time-varying audio effects using only input-output audio recordings, eliminating the need for modulation signal extraction. This approach addresses the challenges of black-box modeling in audio systems, particularly for devices with internal modulation.
- The introduction of this GAN framework is significant as it streamlines the modeling process for audio effects, potentially enhancing the efficiency and accuracy of audio production and sound design. By removing the need for complex control signal extraction, it simplifies workflows for audio engineers.
- This development reflects a broader trend in artificial intelligence where generative models, such as GANs, are increasingly utilized across various domains, including cybersecurity and image processing. The versatility of GANs is evident as they are applied to diverse challenges, from combating malware to improving image resolution, showcasing their potential to transform multiple industries.
— via World Pulse Now AI Editorial System
