# On the Human and Computer Alignment of Attribute-Based Music Matches

arXiv: https://arxiv.org/abs/2609.00987 · HTML: https://mlsanity.com/paper/2609.00987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 57 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Efficient Training of Audio Transformers with Patchout](https://mlsanity.com/paper/2110.05069/) (2021)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [Audio-based Musical Version Identification: Elements and Challenges](https://mlsanity.com/paper/2109.02472/) (2021)
- [Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model](https://mlsanity.com/paper/2401.14542/) (2024)
- [Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio](https://mlsanity.com/paper/2407.14364/) (2024)
- [TensorFlow Audio Models in Essentia](https://mlsanity.com/paper/2003.07393/) (2020)
- [MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI](https://mlsanity.com/paper/2507.03599/) (2025)
- [Quantifying Musical Style: Ranking Symbolic Music based on Similarity to a Style](https://mlsanity.com/paper/2003.06226/) (2020)
- [Discogs-VI: A Musical Version Identification Dataset Based on Public Editorial Metadata](https://mlsanity.com/paper/2410.17400/) (2024)
- [Opening Musical Creativity? Embedded Ideologies in Generative-AI Music Systems](https://mlsanity.com/paper/2508.08805/) (2025)
- [Leveraging Whisper Embeddings for Audio-based Lyrics Matching](https://mlsanity.com/paper/2510.08176/) (2025)
- [Music Plagiarism Detection: Problem Formulation and a Segment-based Solution](https://mlsanity.com/paper/2601.21260/) (2026)
- [Automatic Music Sample Identification with Multi-Track Contrastive Learning](https://mlsanity.com/paper/2510.11507/) (2025)

## Abstract (arXiv)

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.