Acoustic Field

Acoustic field research focuses on understanding and manipulating sound propagation and its interaction with the environment, aiming to accurately model and synthesize sound in complex scenarios. Current efforts concentrate on developing neural network models, including those based on disentangled representations, radiance fields, and diffusion processes, to generate realistic acoustic fields from visual and audio data, often addressing challenges like occlusion and sim-to-real transfer. These advancements are improving audio-visual scene understanding, enabling novel view audio synthesis, and enhancing applications such as robotic navigation and augmented/virtual reality experiences. The development of high-fidelity datasets and improved algorithms is driving progress in this field, with implications for various applications requiring accurate sound modeling and manipulation.