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

arXiv: https://arxiv.org/abs/2605.21538 · HTML: https://mlsanity.com/paper/2605.21538/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2026.

## Built on by (most cited first, 3 of 3)

- [Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation](https://mlsanity.com/paper/2606.07387/) (2026)
- [Improving Text-to-Music Generation with Human Preference Rewards](https://mlsanity.com/paper/2606.21670/) (2026)
- [TTM-Bench: A Framework for Text-to-Music System Performance Benchmarking](https://mlsanity.com/paper/2609.18585/) (2026)

## Abstract (arXiv)

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.