{
  "annotation_id": "a299cf75-dfbc-4e18-b9bb-46aacc2e7022",
  "date_created": "2026-02-17T05:53:12.545000Z",
  "date_modified": "2026-02-17T05:53:12.545000Z",
  "file_hash": "8b09ee529243af6bc591564f9dba93d1a3d37b33d2ddc53d939ec1601e2e3549",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.07237",
    "authors": [
      "Guobin Ma",
      "Yuxuan Xia",
      "Jixun Yao",
      "Huixin Xue",
      "Hexin Liu",
      "Shuai Wang",
      "Hao Liu",
      "Lei Xie"
    ],
    "categories": [
      "eess.AS",
      "cs.SD"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "The ICASSP 2026 Automatic Song Aesthetics Evaluation Challenge",
    "url": "https://arxiv.org/abs/2601.07237",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "445cf0fd-eee6-4220-bdbe-df3c5b16303d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}