Open Access
| Issue |
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
|
|
|---|---|---|
| Article Number | 01026 | |
| Number of page(s) | 6 | |
| Section | Artificial Intelligence, Big Data and Computer Science | |
| DOI | https://doi.org/10.1051/itmconf/20268801026 | |
| Published online | 27 July 2026 | |
- S. Natale and A. L. Guzman, Reclaiming the human in machine cultures: Introduction. Media Cult. Soc. 44, 016344372210996 (2022) [Google Scholar]
- E. E. Harding, J. C. Kim, A. P. Demos, I. R. Roman, P. Tichko, C. Palmer, E. W. Large, Musical neurodynamics. Nat. Rev. Neurosci. 26, 293–307 (2025) [Google Scholar]
- J. Copet, F. Kreuk, I. Gat, T. Remez, D. Kant, G. Synnaeve, Y. Adi and A. Défossez, Simple and controllable music generation. Adv. Neural Inf. Process. Syst. 36, 47704–47720 (2024) [Google Scholar]
- H. Purwins, B. Li, T. Virtanen, J. Schlüter, S. Y. Chang, and T. Sainath, Deep learning in audio signal processing. IEEE J. Sel. Top. Signal Process. 13, 206–219 (2019) [Google Scholar]
- R. B. Dannenberg, O2: a network protocol of music systems. Wirel. Commun. Mob. Comput. 2019, 1-13 (2019) [Google Scholar]
- A. Jangda, B. Powers, E. D. Berger, A. Guha, Not so fast: Performance analysis of WebAssembly against native code, in the Proceedings of the USENIX Annual Technical Conference, Renton, WA, USA, July 10-12, 107-120. (2019) [Google Scholar]
- Qwen Team, Qwen2.5-Omni technical report. ArXiv abs/2503.20215 (2025) [Google Scholar]
- L. Della Libera, F. Paissan, C. Subakan, and M. Ravanelli, FocalCodec-stream: Streaming low-bitrate speech coding through causal distillation. ArXiv abs/2509.16195 (2025) [Google Scholar]
- Y. Kim, S. Brade, A. Wang, D. Zhou, H. Kim, B. Wang, S. -J. Lee, H. F. Garcia, C. -Z. A. Huang, C. Donahue, A design space of live music agents, in the Proceedings of the CHI Conference on Human Factors in Computing Systems, Barcelona, Spain, April 13-17, 1-15. (2026) [Google Scholar]
- A. Scarlatos and Y. Wu and I. Simon and A. Roberts, ReaLJam: Real-time human-AI music jamming using reinforcement learning-tuned transformers. ArXiv abs/2504.03206 (2025) [Google Scholar]
- F. Caspe and J. Shier and M. Sandler and C. Saitis and A. McPherson, Design of neural synthesizers to support low-latency interaction. J. Audio Eng. Soc. 73, 240–255 (2025) [Google Scholar]
- Y. Chung and P. Eu and J. Lee and K. Choi and J. Nam and B. S. Chon, KAD: No more FAD! An effective and efficient metric to evaluate audio generation. ArXiv abs/2502.15602 (2025) [Google Scholar]
- M. Frohmann and E. Epure and G. Meseguer-Brocal and M. Schedl and R. Hennequin, AI-generated song detection based on lyrics transcripts, in the Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), San Francisco, CA, USA, November 10-14 (2025) [Google Scholar]
- R. Mitra and I. Zualkernan, A systematic review on music generation using deep learning and generative AI. IEEE Access 13, 18079–18106 (2025) [Google Scholar]
- V. Zappi and K. Tatar, Neural audio instruments: epistemological and phenomenological approaches to the musical embodiment of deep learning. Front. Comput. Sci. 7, 1575168 (2025) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.