# Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio

arXiv: https://arxiv.org/abs/2407.14364 · HTML: https://mlsanity.com/paper/2407.14364/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 19 Jul 2024.

## Builds on (most cited first, 20 of 20)

- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [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)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Simple and Controllable Music Generation](https://mlsanity.com/paper/2306.05284/) (2023)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Efficient Training of Audio Transformers with Patchout](https://mlsanity.com/paper/2110.05069/) (2021)
- [Fast Timing-Conditioned Latent Audio Diffusion](https://mlsanity.com/paper/2402.04825/) (2024)
- [MusicLDM: Enhancing Novelty in Text-to-Music Generation Using Beat-Synchronous Mixup Strategies](https://mlsanity.com/paper/2308.01546/) (2023)
- [Adapting Frechet Audio Distance for Generative Music Evaluation](https://mlsanity.com/paper/2311.01616/) (2023)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [VampNet: Music Generation via Masked Acoustic Token Modeling](https://mlsanity.com/paper/2307.04686/) (2023)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model](https://mlsanity.com/paper/2401.14542/) (2024)
- [Generation or Replication: Auscultating Audio Latent Diffusion Models](https://mlsanity.com/paper/2310.10604/) (2023)
- [TensorFlow Audio Models in Essentia](https://mlsanity.com/paper/2003.07393/) (2020)
- [A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music](https://mlsanity.com/paper/2308.13736/) (2023)
- [Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners](https://mlsanity.com/paper/1807.03046/) (2018)

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?](https://mlsanity.com/paper/2505.19663/) (2025)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection](https://mlsanity.com/paper/2505.20979/) (2025)
- [Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation](https://mlsanity.com/paper/2507.17937/) (2025)
- [Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems](https://mlsanity.com/paper/2510.08062/) (2025)
- [Mitigating data replication in text-to-audio generative diffusion models through anti-memorization guidance](https://mlsanity.com/paper/2509.14934/) (2025)
- [Prevailing Research Areas for Music AI in the Era of Foundation Models](https://mlsanity.com/paper/2409.09378/) (2024)
- [On the de-duplication of the Lakh MIDI dataset](https://mlsanity.com/paper/2509.16662/) (2025)
- [On the Human and Computer Alignment of Attribute-Based Music Matches](https://mlsanity.com/paper/2609.00987/) (2026)
- [TrueMuse: A Benchmark for Data Attribution in Text-to-Music Models](https://mlsanity.com/paper/2610.00835/) (2026)

## Abstract (arXiv)

Recent advancements in music generation are raising multiple concerns about the implications of AI in creative music processes, current business models and impacts related to intellectual property management. A relevant discussion and related technical challenge is the potential replication and plagiarism of the training set in AI-generated music, which could lead to misuse of data and intellectual property rights violations. To tackle this issue, we present the Music Replication Assessment (MiRA) tool: a model-independent open evaluation method based on diverse audio music similarity metrics to assess data replication. We evaluate the ability of five metrics to identify exact replication by conducting a controlled replication experiment in different music genres using synthetic samples. Our results show that the proposed methodology can estimate exact data replication with a proportion higher than 10%. By introducing the MiRA tool, we intend to encourage the open evaluation of music-generative models by researchers, developers, and users concerning data replication, highlighting the importance of the ethical, social, legal, and economic consequences. Code and examples are available for reproducibility purposes.