Introduction

Music can touch the hearts of any audience without them possessing any knowledge of its context. The power of music is transcendental, and it stems from the timbre of the instrument(s), the fundamental rhythmic structure and melody, the dynamics, instrumentation, and many more, all of which cooperate in some form of harmony to create the final product. With the recent rise of Artificial Intelligence-Generated Contents (AIGC), AI for music is a promising field full of creativity, novel methodologies, and technologies that are yet to be explored. Currently, AI for music methods have been commonly concentrated on utilizing machine learning and deep learning techniques to generate new music. Despite the significant milestones that have been achieved thus far, many are not necessarily robust for a wide range of applications.

AI music itself is a timely topic. This workshop aims to generate momentum around this topic of growing interest, and to encourage interdisciplinary interaction and collaboration between AI, music, Natural Language Processing (NLP), machine learning, multimedia, Human-Computer Interaction (HCI), audio processing, computational linguistics, and neuroscience. It serves as a forum to bring together active researchers and practitioners from academia and industry to explore emerging advances in AI music generation, including generative models, multimodal music intelligence, music representation and understanding, human-AI co-creation, evaluation, and creative applications in this promising area.

Topics

Machine learning/AI for music
Natural language processing for music generation
Algorithmic music generation
Music generation based on a specific aspect: lyrical, chordal, motivic, melodic, and rhythmic
AI-generated lyrics
AI-generated instrumental audio (including vocal)
Computational musicology
AI music interpretation
AI music data representation
Music evaluation metrics
Multiple-channel AI music generation
AI musical fusion (notes, audio, etc.)
AI generation for musical performance and expression
AI music enhancement (e.g. AI-generated instrumentation)
AI musical ethics
AI music generation datasets
Human-Centered Interaction (HCI) for AI music generation
AI music for neuroscientific applications
AI-aided music theory applications
AI bird song generation and translation
AI natural sound generation

Important Dates

Cross-AI Deadlines

See Important Dates for all Cross-AI paper and abstract submission deadlines.

IEEE Big Data Workshop Deadlines

IEEE Big Data Workshop Deadline	IEEE Workshop Milestone
October 19, 2026	Full/Short Paper Submission
October 26, 2026	Poster/Demo Paper Submission
November 2, 2026	Acceptance Notification
November 14, 2026	Pre-recorded Video Submission; Camera-Ready Submission
November 14, 2026	IEEE Author Registration Deadline
December 14–16, 2026	IEEE Big Data Workshops & Cross-AI Symposium 2026

Archival papers submitted to the IEEE Big Data Workshop must meet these deadlines.

AI Music Composition Deadlines (AoE)

Milestone	IEEE Big Data	Cross-AI
AI musical composition submission	October 26, 2026	November 9, 2026
Music acceptance notification	November 9, 2026	November 23, 2026

AI Music Composition Submission

The workshop also accepts AI musical compositions.

Item	Requirement
Abstract	1–2 pages + up to 2-page appendix
Audio or video	Shareable MP3 or MP4 link, maximum 10 minutes
Sheet music	Optional, up to 9 pages
Total length	Abstract (including appendix) and sheet music combined: no more than 10 pages

Please note that composition submissions not created using AI or algorithmic methods, or including audio exceeding 10 minutes in duration, will not be eligible for participation in the AI Music Composition Competition.

Author and Submission Guidelines

Please follow Author and Submission Guidelines to submit your work.

All submissions are made through the AirBalloon conference management system and evaluated through a unified process. Upon acceptance, authors may choose to publish in the IEEE Big Data conference proceedings or the indexed Cross-AI conference proceedings, with the corresponding registration fees. Please refer to the respective official registration pages (Cross-AI registration and the IEEE Big Data 2026 website) for current fees and registration details. At least one author must register for the corresponding event for the paper to be published. Abstract submissions are non-archival: not published in the proceedings; abstract posted on the conference platform and in the Cross-AI non-archival repository; previously published work is posted on the conference platform only.

Committee

Workshop Chairs

Ellie Zhang, IntelliSky, USA
Callie Liao, Stanford University, USA

Program Committee

Zhiqian Chen, Mississippi State University, USA
Shlomo Dubnov, UC San Diego, USA
Kaiqun Fu, Texas Christian University, USA
Jesse Guessford, George Mason University, USA
Ge Jin, Purdue University, USA
Fanchun Jin, Google, USA
Lindi Liao, George Mason University, USA
Sean Luke, George Mason University, USA
Jeffrey Morris, Texas A&M University, USA
Chen Shen, Google, USA
Alex Wong, Yale University, USA
Yanjia Zhang, IntelliSky, USA

AIMG Group

This group serves as a forum for notices and announcements of interest to the AI Music Generation (AIMG) community. This includes news, events, calls for papers, calls for collaborations between academia and industry, dataset releases, employment-related announcements, etc.

Welcome to subscribe to the AIMG group.

Previous Workshops

If you are interested in serving on the workshop program committee or paper reviewing, please apply online.