Audio generation is a generative technology that uses artificial intelligence models to automatically synthesize sound content. Its goal is to generate audio signals such as music, speech, ambient sounds, and sound effects based on text descriptions, audio conditions, or other forms of input. This technology integrates generative models, speech processing, music information retrieval, and multimodal learning, and is an important component of the field of AI-driven content generation.

The development of audio generation builds upon long-term research in multiple fields, including digital signal processing, speech synthesis, and music generation. Early methods primarily relied on rule-based systems, signal processing algorithms, and sample-based splicing, limiting the richness and naturalness of the generated content. With the advancement of deep learning and generative models, researchers began exploring the use of neural networks to directly learn the distribution of sound data, enabling more flexible audio synthesis. In 2022, researchers from Meta AI and the Hebrew University of Jerusalem published a paper... AudioGen: Textually Guided Audio Generation The AudioGen model was proposed, which generates general audio content through text conditional control. It explores an end-to-end modeling method from natural language description to sound generation and has become one of the important representative studies in the field of text-to-audio generation.

Modern audio generation systems typically rely on techniques such as diffusion models, autoregressive models, or neural audio codecs to map text prompts, musical descriptions, or other conditional information into audio representations, and further generate continuous sound signals. In recent years, with the development of large-scale multimodal models, audio generation technology has continuously improved in terms of sound quality, generation diversity, and conditional control capabilities. Currently, this technology is widely used in film and television scoring, game sound effects, advertising creation, virtual characters, music-assisted composition, and human-computer interaction, providing a new technological path for automated audio content production.