Audio Driven

Audio-driven research focuses on understanding and generating audio signals, often in conjunction with other modalities like text and video. Current efforts concentrate on developing robust models for tasks such as audio-visual representation learning, talking head synthesis (using diffusion models and autoencoders), and audio-to-text/text-to-audio generation (leveraging large language models and neural codecs). These advancements have significant implications for various fields, including film-making, virtual reality, assistive technologies, and multimedia forensics, by enabling more realistic and interactive audio-visual experiences and improving analysis of audio-visual data.