Problem
Research questions and friction points this paper is trying to address.
generative AI
musical attributes
human judgments
audio-based similarity metrics
perceptual experiment
On the Human and Computer Alignment of Attribute-Based Music Matches
🤖 AI Summary
研究通过设计包含五个音乐属性的感知实验，收集专家对音乐匹配的评估，以解决AI生成音乐与人类判断的一致性问题。
📝 Abstract
Recent advances in generative AI are raising ethical concerns regarding the originality of generated content and the potential replication of training data, with further implications for transparency, attribution, and intellectual property. In music, several computational approaches have been proposed to identify potential replication, using audio-based similarity metrics. Yet, their alignment with human judgments across distinct musical attributes remains underexplored. To address this gap, we conduct a perceptual experiment on music matches, defined as strongly similar musical excerpts. We focus on five musical attributes: melody, harmony, rhythm, voice, and timbre. We design a triplet-based forced-choice task comprising 300 cases, including plagiarism examples, cover songs, and AI-generated music. From this experiment, we introduce the MATCHA (Musical Attribute-based Triplet Comparison with Human Annotations) dataset: a collection of 1105 perceptual assessments of attribute-based music matches from 83 expert participants. Our findings reveal measurable agreement among participants in identifying matches across attributes. We further observe partial alignment between human judgments and computational similarity measures. Overall, this work underscores the importance of domain-specific and perceptually grounded evaluation frameworks for generative AI in creative practice.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Musical Attribute-based Triplet Comparison
Human Annotations
Computational Similarity Measures
Perceptual Evaluation
Creative Practice
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