Audio Signal Processing in the Artificial Intelligence Era: Challenges and Directions

Artificial intelligence (AI) has seen significant advancement in recent years, leading to increasing interest in integrating these techniques to solve both existing and emerging problems in audio engineering. In this paper, the authors investigate current trends in the application of AI for audio engineering, outlining open problems and applications in the research field. The paper begins by providing an overview of AI-based algorithm development in the context of audio, discussing problem selection and taxonomy. Next, human-centric AI challenges and how they relate to audio engineering are explored, including ethics, trustworthiness, explainability, and interaction, emphasizing the need for ethically sound and human-centered AI systems. Subsequently, technical challenges that arise when applying modern AI techniques to audio are examined, including robust generalization, audio quality, high sample rates, and real-time processing with low latency. Finally, the authors outline applications of AI in audio engineering, covering the development of machine learning–powered audio effects, synthesizers, automated mixing systems, and spatial audio, speech enhancement, dialog separation, and music generation. Emphasized are the need for a balanced approach that integrates humancentric concerns with technological advancements, advocating for responsible and effective application of AI.