Understanding Human Perception of Music Plagiarism Through a Computational Approach

TL;DR

This paper investigates how humans perceive music plagiarism by analyzing key musical features and proposes a novel LLM-based framework to systematically evaluate musical similarity from a human perception perspective.

Contribution

It identifies the key criteria humans use to perceive music plagiarism and introduces a systematic LLM-based approach for evaluating musical similarity.

Findings

Humans focus on melody, rhythm, and chord progression in plagiarism perception.

The LLM-as-a-judge framework effectively models human perception criteria.

Key features and variation levels influence perceived musical similarity.

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.