Finding the noise: Zero-shot AI Music Detection

2026-07-28

AI Summary

This work addresses the regulatory challenges posed by the proliferation of AI-generated music from unknown models by proposing an unsupervised, zero-shot detection framework that effectively distinguishes authentic from synthetic audio without relying on prior knowledge of generative models. The method integrates artifact-based feature extraction, non-negative matrix factorization (NMF), and a combination of one-class classification with unsupervised clustering strategies, thereby introducing zero-shot learning to AI music detection for the first time in a systematic manner. Experimental results demonstrate that the approach achieves strong performance in both binary authenticity discrimination and multi-class clustering of unseen AI-generated music sources, making it well-suited for monitoring high-purity synthetic content at scale within large music repositories.

Abstract

We present a novel method for AI-generated music detection in scenarios where the models that generated the input samples are unknown to the detector (e.g., from a newly released service). Since 2023, there has been a multiplication of user-friendly AI-music generation services (e.g., Suno, Udio), along with regular updates and new features. There is thus a need to address synthetic content detection in an unsupervised way to adapt to this rapidly changing context. This angle has not been much studied in music yet. We propose to study two tasks. First, discriminating between real and synthetic music. This may be approached in a one-class manner, namely, using some baseline real music and trying to determine what falls outside. Second, zero-shot multi-class identification, which is more similar to an unsupervised clustering task on a mix of real and various AI-music generations, where the goal is to create coherent, high-purity clusters. We propose a combination of a previously proposed artifact-extraction method, on top of which we apply non-negative matrix factorization and simple classification and clustering methods. We achieve excellent performance on both tasks, showing that the proposed methods may be used to monitor large-scale catalogs that may receive AI-generated samples from various newly released generative models.

Problem

• AI-generated music detection
• zero-shot
• unsupervised
• music clustering
• synthetic content detection

Innovation

• zero-shot detection
• AI-generated music
• unsupervised clustering
• non-negative matrix factorization
• artifact extraction