Waveform Generation

Waveform generation focuses on creating artificial signals, primarily audio, with high fidelity and efficiency. Current research emphasizes developing novel generative models, such as flow-based models (including those incorporating multi-period estimations and adversarial training), diffusion models (enhanced with techniques like Griffin-Lim algorithm integration), and transformer-based architectures, to improve the quality, speed, and controllability of waveform synthesis. These advancements have implications for various applications, including speech synthesis, music generation, and even gravitational wave detection, by enabling more realistic and efficient signal creation.