The ICASSP 2026 Automatic Song Aesthetics Evaluation Challenge
Guobin Ma, Yuxuan Xia, Jixun Yao, Huixin Xue, Hexin Liu, Shuai Wang, Hao Liu, Lei Xie

TL;DR
The ICASSP 2026 ASAE Challenge aims to evaluate AI-generated songs' aesthetic quality, fostering progress in aligning automated metrics with human musical preferences through a standardized benchmark.
Contribution
This paper introduces a new challenge with two tracks for predicting subjective aesthetic scores of AI-generated music, establishing a benchmark for future research.
Findings
Top systems outperformed baseline significantly
Progress in human-aligned music evaluation methods
Established a standardized benchmark for song aesthetics
Abstract
This paper summarizes the ICASSP 2026 Automatic Song Aesthetics Evaluation (ASAE) Challenge, which focuses on predicting the subjective aesthetic scores of AI-generated songs. The challenge consists of two tracks: Track 1 targets the prediction of the overall musicality score, while Track 2 focuses on predicting five fine-grained aesthetic scores. The challenge attracted strong interest from the research community and received numerous submissions from both academia and industry. Top-performing systems significantly surpassed the official baseline, demonstrating substantial progress in aligning objective metrics with human aesthetic preferences. The outcomes establish a standardized benchmark and advance human-aligned evaluation methodologies for modern music generation systems.
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Taxonomy
TopicsMusic Technology and Sound Studies · Music and Audio Processing · Artificial Intelligence in Games
