♪  ICME Workshop  ·  2025

Artificial Intelligence
for Music

Examining the dynamic intersection of AI and music — composition, performance, production, collaboration, and audience engagement.

Submit Paper → ICME 2025 Conference AMT Challenge
Workshop date: June 30, 2025 (Monday) — ICME 2025

Summary

This one-day workshop at the 2025 IEEE ICME Annual Conference investigates how AI is transforming music composition, performance, production, collaboration, and audience engagement. Topics encompass AI-driven composition, sound design, remixing, mastering, legal and ethical considerations, and the impact on music education.

The organizing team hosts a competitive challenge for Automatic Music Transcription (AMT) accepting worldwide submissions from academia and industry, with winners presenting their solutions at this workshop.

Call for Papers

We invite submissions examining the dynamic intersection of artificial intelligence and multimedia with an emphasis on music and audio technologies. The workshop covers music creation, recognition, education, ethical and legal implications, and business opportunities.

Topics of Interest

Music Composition & Generation
Music Practice & Performance
Recognition & Transcription
Sound Design Applications
AI-Generated Music Videos
AI-Generated Lyrics
Legal & Ethical Implications
Musicians’ Career Impacts
AI-Assisted Music Education
Business Opportunities
Music Datasets & Analysis

Submission Requirements

  • Follow ICME 2025 author guidelines
  • Maximum 6 pages (text, figures, references)
  • Double-blind review process — authors must not self-identify in PDFs
  • Work-in-progress welcome; prototype implementations encouraged
  • No previously published or substantially similar work
  • No concurrent submissions to other conferences or journals

Submit via the CMT Submission Portal →

Accepted papers will be posted on the workshop website and published on IEEE Xplore.

Important Dates

Apr 1, 2025
Submission deadline (11:59 PM Pacific Time)
Apr 25, 2025
Acceptance notification
May 15, 2025
Final version due
Jun 30, 2025
Workshop at ICME 2025

Workshop Schedule — June 30, 2025

09:30 AM
Welcome by Yung-Hsiang Lu and Kristen Yeon-Ji Yun
09:35 AM
Keynote: Zhiyao Duan (Moderator: Yeon-Ji Yun)
10:15 AM
Invited Talk: Fatemeh Jamshidi (Moderator: Yeon-Ji Yun)
10:50 AM
Break
11:00 AM
Invited Talk: Gus Xia (Moderator: Emmanouil Benetos)
11:30 AM
Invited Talk: Geoffroy Peeters (Moderator: Emmanouil Benetos)
12:00 PM
Invited Talk: Emmanouil Benetos (Moderator: Zhiyao Duan)
12:30 PM
Lunch break
02:00 PM
Paper Presentations (Moderator: Zhiyao Duan)
03:30 PM
Panel Discussion (Moderator: Gus Xia; Panelists: Peeters, Benetos, Duan, Ziyu Wang)
04:30 PM
Transcription Challenge Winners (Moderator: Yung-Hsiang Lu)
05:00 PM
Adjourn

Invited Speakers

Zhiyao Duan
Zhiyao Duan
Associate Professor, Electrical and Computer Engineering, Computer Science, and Data Science, University of Rochester
Co-founder of Violy (AI-based music education). President of ISMIR; NSF CAREER award recipient. Research spans computer audition and connections with computer vision, NLP, and AR/VR. Senior area editor for IEEE Signal Processing Letters.
Fatemeh Jamshidi
Fatemeh Jamshidi
Assistant Professor, Department of Computer Science, Cal Poly Pomona
PhD Computer Science and Master’s in Music Education from Auburn University. Research spans AI, computer music, ML/deep learning in music, game AI, human-AI collaboration, AR/VR. Founded Computing + Music programs serving hundreds from underrepresented groups since 2018.
Gus Xia
Gus Xia
Assistant Professor, Machine Learning, Mohamed bin Zayed University of Artificial Intelligence
Designs interactive intelligent systems extending human musical creation and expression. Research intersects machine learning, HCI, robotics, and computer music. Notable works include interactive composition via style transfer, autonomous dancing robots, and haptic guidance for flute tutoring.
Geoffroy Peeters
Geoffroy Peeters
Full Professor, Laboratoire Traitement et Communication de l’Information (S2A team), Télécom Paris
“Self-Supervised Learning for Invariant and Equivariant Representations”
PhD and Habilitation from University Paris-VI. Previously led Music Information Retrieval research at IRCAM. Discusses advances in self-supervised learning for music covering invariance paradigms and equivariance, presenting contributions including MatPac, Stem-JEPA, PESTO, and CPC.
Emmanouil Benetos
Emmanouil Benetos
Reader in Machine Listening & Director of Research, School of Electronic Engineering and Computer Science, Queen Mary University of London
“Machine Learning Paradigms for Music and Audio Understanding”
Member of Centre for Digital Music and Centre for Multimodal AI. Deputy Director of UKRI Centre for Doctoral Training in AI and Music (AIM). Research covers computational audio analysis from signal processing and supervised learning to recent foundation model approaches.

Accepted Papers

Analysis of Improvised Jazz Melodies Using Harmonic Tags
Carey Bunks (Queen Mary University of London); Simon Dixon (Queen Mary University of London); Bruno Di Giorgi (Apple)
Exploiting Music Source Separation for Automatic Lyrics Transcription with Whisper
Jaza Syed, Ivan Meresman Higgs, Ondřej Cífka (AudioShake), Mark Sandler — Queen Mary University of London
M6(GPT)3: Generating Multitrack Modifiable Multi-Minute MIDI Music from Text using Genetic Algorithms, Probabilistic Methods and GPT Models
Jakub Poćwiardowski, Mateusz Modrzejewski (Warsaw University of Technology); Marek S. Tatara (Gdansk University of Technology)
AI Music Artist Toolkit (AIMAT) — A Modular Environment for Experimenting with AI in Music
Eric Browne (MTU); Michael Clemens (New Jersey Institute of Technology)

Transcription Challenge Winners

1st Place
MIROS (Music Information Retrieval Osnabrück)
Deniz Gün, Fernando Riveros, Paul Koesling, Anish Haluvani Sundresh, William Shelor
2nd Place
YourMT3-YPTF-MoE-M
Sungkyun Chang, Simon Dixon, Emmanouil Benetos

Workshop Organizers

Yung-Hsiang Lu
Yung-Hsiang Lu
Professor of Electrical and Computer Engineering, Purdue University
IEEE Fellow; ACM Distinguished Scientist. Publications in AI Magazine, Nature Machine Learning, and Computer. Editor of “Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence” (Chapman & Hall, 2022).
Kristen Yeon-Ji Yun
Kristen Yeon-Ji Yun
Clinical Associate Professor of Music, Purdue University
PI of “AI Technology for Future Music Performers” (NSF IIS 2326198). Active international soloist and clinician with tours across Malaysia, Germany, Japan, China, Spain, France, Italy, South Korea, and more.
George K. Thiruvathukal
George K. Thiruvathukal
Professor & Chair, Computer Science, Loyola University Chicago; Visiting Scientist, Argonne National Laboratory
Research in high-performance computing, distributed systems, software engineering, machine learning, digital humanities, and music. Author of “Software Engineering for Science” and multiple computing textbooks.

Technical Program Committee

Charalampos Saitis
Charalampos Saitis
Lecturer in Digital Music Processing, Queen Mary University of London
Leads Communication Acoustics Lab (COMMA) at Centre for Digital Music. Co-investigator UKRI CDT AI and Music (2019–2028). Expertise in cognitive science, music informatics, generative AI in sonic creativity.
Hao-Wen (Herman) Dong
Hao-Wen (Herman) Dong
Assistant Professor, Performing Arts Technology, University of Michigan
PhD from UC San Diego. Research goal: empower music and audio creation with machine learning. Awards: UCSD CSE Doctoral Excellence, KAUST Rising Stars in AI, ICASSP Rising Stars in Signal Processing.
Mei-Ling Shyu
Mei-Ling Shyu
Professor, Electrical and Computer Engineering, University of Missouri–Kansas City
PhD and three Master’s degrees from Purdue University. Research in data science, AI, machine learning, big data analytics, multimedia information systems, and semantic-based information retrieval.
Wen-Huang Cheng
Wen-Huang Cheng
Distinguished Chair Professor, Computer Science and Information Engineering, National Taiwan University
Founding director of AIMM Research Group. IEEE Fellow; Asia-Pacific Artificial Intelligence Association Fellow. Former Distinguished Professor at National Yang Ming Chiao Tung University.