人工智能在音频信号处理中的应用与挑战  

The Application and Challenge of Artificial Intelligence in Audio Signal Processing

作　　者：李粟 LI Su(Tianjin Municipal Tax Service,State Taxation Administration,Tianjin 300000,China)

机构地区：[1]国家税务总局天津市税务局,天津300000

出　　处：《电声技术》2023年第8期45-47,51,共4页Audio Engineering

摘　　要：随着人工智能(Artificial Intelligence,AI)技术的迅猛发展,音频信号处理成为引人瞩目的研究领域。在应用现状方面,它主要聚焦于自适应音频分析与语音识别、音频合成与音乐创作、高级音频增强技术的创新以及实时音频处理与自动化应用的前沿。然而,这一领域也面临着数据隐私和伦理挑战,在解决挑战的过程中,研究提出独特且具有前瞻性的策略。面向多领域数据的跨模态学习与迁移学习,强调强化学习和元学习在实时音频处理中的应用,提出将可解释的深度学习模型与隐私保护技术融合。策略的提出不仅为音频信号处理领域的未来发展指明了方向,也为AI在其他领域的应用提供了有益借鉴。With the rapid development of Artificial Intelligence(AI)technology,audio signal processing has become an eye-catching research field.In terms of application status,it focuses on the innovation of adaptive audio analysis and speech recognition,audio synthesis and music creation,advanced audio enhancement technology,and the forefront of real-time audio processing and automation applications.However,this field also faces challenges in data privacy and ethics,and in addressing these challenges,research has proposed unique and forward-looking strategies.Cross modal learning and transfer learning for multi domain data,as well as emphasizing the application of reinforcement learning and meta learning in real-time audio processing.It is particularly important to propose the integration of interpretable deep learning models and privacy protection technologies.The proposal of the strategy not only points out the direction for future development in the field of audio signal processing,but also provides beneficial references for the application of artificial intelligence in other fields.

关 键 词：人工智能(AI) 音频信号处理 跨模态学习 可解释性 隐私保护技术 

分 类 号：TN912.3[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]