Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods
This challenge lowers the barrier for academic research in text-to-music generation by providing standardized datasets, baselines, and evaluation methods, addressing the current dominance of industrial-scale proprietary models.
The paper introduces the ICME 2026 Grand Challenge on Academic Text-to-Music Generation (ATTM), which establishes a benchmark using a CC-licensed subset of MTG-Jamendo to enable academic research in text-to-music generation. The challenge includes two tracks (Efficiency and Performance) and evaluates submissions via objective metrics (FAD, CLAP, CCS) and subjective listening tests.
This paper presents an overview and the technical framework of the ICME 2026 Grand Challenge on Academic Text-to-Music Generation (ATTM). Despite the rapid progress in text-to-music generation (TTM) systems, the field is currently dominated by models trained on massive proprietary datasets with industrial-scale computational resources, creating a significant barrier for academic research. To address this, the ATTM Challenge establishes a fair-play benchmark that requires participants to train generative models strictly from scratch using a standardized, CC-licensed subset of the MTG-Jamendo dataset containing only instrumental music. The challenge is divided into two tracks: the Efficiency Track (limited to 500M parameters) and the Performance Track (no parameter limit). Submissions are evaluated through a multi-stage process involving objective metrics, including Frechet Audio Distance, CLAP score, and a novel Concept Coverage Score (CCS), followed by a subjective listening test. By providing open-source baselines, preprocessing pipelines, reference captions, and public evaluation code for computing FAD and CLAP, this challenge aims to facilitate and promote TTM research in academic contexts.