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

Steinmetz CJ, Uhle C, Everardo F, Mitcheltree C, McElveen JK, Jot JM, Wichern G. Audio Signal Processing in the Artificial Intelligence Era: Challenges and Directions. J Audio Eng Soc. 2025;73(7/8):406-428. doi:10.17743/jaes.2022.0209

@article{Steinmetz2025_22921,
  author = {Steinmetz, Christian J. and Uhle, Christian and Everardo, Flavio and Mitcheltree, Christopher and McElveen, J. Keith and Jot, Jean-Marc and Wichern, Gordon},
  title = {{Audio Signal Processing in the Artificial Intelligence Era: Challenges and Directions}},
  journal = {Journal of the Audio Engineering Society},
  volume = {73},
  number = {7/8},
  pages = {406--428},
  year = {2025},
  month = jul,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0209},
  url = {https://doi.org/10.17743/jaes.2022.0209}
}

TY  - JOUR
TI  - Audio Signal Processing in the Artificial Intelligence Era: Challenges and Directions
AU  - Steinmetz, Christian J.
AU  - Uhle, Christian
AU  - Everardo, Flavio
AU  - Mitcheltree, Christopher
AU  - McElveen, J. Keith
AU  - Jot, Jean-Marc
AU  - Wichern, Gordon
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 73
IS  - 7/8
SP  - 406
EP  - 428
PY  - 2025
DA  - 2025/07/07
DO  - 10.17743/jaes.2022.0209
UR  - https://doi.org/10.17743/jaes.2022.0209
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -