Authors
- Roser Batlle-Roca*
- Wei-Hsiang Liao
- Xavier Serra
- Yuki Mitsufuji
- Emilia Gómez*
* External authors
Venue
- ISMIR 2024
Date
- 2024
Share
Towards Assessing Data Replication in Music Generation with Music Similarity Metrics on Raw Audio
Roser Batlle-Roca*
Xavier Serra
Emilia Gómez*
* External authors
ISMIR 2024
2024
Abstract
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 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 of the training set. We evaluate the ability of five metrics to identify exact replication, by conducting a controlled replication experiment in different music genres based on 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 ethical, social, legal and economic consequences of generative AI in the music domain.
Related Publications
Although recent speech processing technologies have achieved significant improvements in objective metrics, there still remains a gap in human perceptual quality. This paper proposes Diffiner, a novel solution that utilizes the powerful generative capability of diffusion mod…
Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing visual-acoustic correlations without considering the physical factors that shape real-world sounds…
MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data. In practice, it is often used as a latent MF by leveraging the pre-trained Stable Diffusion variational autoencoder (SD-VAE)…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.
