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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Accesso online: | https://arxiv.org/abs/2605.21538 |
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| _version_ | 1866910242195898368 |
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| author | Hsieh, Fang-Chih Lee, Wei-Jaw Wang, Chun-Ping Lee, Hung-yi Dong, Hao-Wen Yang, Yi-Hsuan |
| author_facet | Hsieh, Fang-Chih Lee, Wei-Jaw Wang, Chun-Ping Lee, Hung-yi Dong, Hao-Wen Yang, Yi-Hsuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21538 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods Hsieh, Fang-Chih Lee, Wei-Jaw Wang, Chun-Ping Lee, Hung-yi Dong, Hao-Wen Yang, Yi-Hsuan Sound 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. |
| title | Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods |
| topic | Sound |
| url | https://arxiv.org/abs/2605.21538 |