{"ID":2822746,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.02586","arxiv_id":"2601.02586","title":"Understanding Human Perception of Music Plagiarism Through a Computational Approach","abstract":"There is a wide variety of music similarity detection algorithms, while discussions about music plagiarism in the real world are often based on audience perceptions. Therefore, we aim to conduct a study to examine the key criteria of human perception of music plagiarism, focusing on the three commonly used musical features in similarity analysis: melody, rhythm, and chord progression. After identifying the key features and levels of variation humans use in perceiving musical similarity, we propose a LLM-as-a-judge framework that applies a systematic, step-by-step approach, drawing on modules that extract such high-level attributes.","short_abstract":"There is a wide variety of music similarity detection algorithms, while discussions about music plagiarism in the real world are often based on audience perceptions. Therefore, we aim to conduct a study to examine the key criteria of human perception of music plagiarism, focusing on the three commonly used musical feat...","url_abs":"https://arxiv.org/abs/2601.02586","url_pdf":"https://arxiv.org/pdf/2601.02586v1","authors":"[\"Daeun Hwang\",\"Hyeonbin Hwang\"]","published":"2026-01-05T22:37:19Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.IR\"]","methods":"[\"Large Language Model\"]","has_code":false}