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arXiv · 2605.21538

Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods

Fang-Chih Hsieh·Wei-Jaw Lee·Chun-Ping Wang·Hung-yi Lee·Hao-Wen Dong·Yi-Hsuan Yang
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Abstract

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.

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Fang-Chih Hsieh, Wei-Jaw Lee, Chun-Ping Wang, Hung-yi Lee, Hao-Wen Dong, Yi-Hsuan Yang. 2026-05-20. Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods. https://arxiv.org/abs/2605.21538

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