Detection of AI-generated stems within hybrid human-AI music

2026-07-29

AI Summary

This study presents the first exploration into the detectability of AI-generated tracks within music co-created by humans and artificial intelligence. Addressing the challenge that general-purpose source separation methods often fail to reliably recover AI-specific artifacts, the authors propose a parallel detection architecture that operates without requiring full track separation. The approach leverages a neural audio codec to simulate the mixing process and combines short-time audio block analysis, relative energy estimation, and a binary classifier to directly identify AI-generated components within the mixed audio signal. Experiments on the MUSDB18-HQ dataset demonstrate promising track-level detection performance, confirming the feasibility of effectively discerning AI-generated content even in complex musical mixtures.

Abstract

This paper presents, to the best of our knowledge, the first study on detecting human-AI hybrid music tracks created by mixing human-produced and AI-generated stems. Building on recent work showing that AI music detectors can identify decoder-related artifacts in fully generated music, we investigate whether such artifacts remain detectable at the stem level after mixing. Using MUSDB18-HQ database in a two-stem vocals + accompaniment setting, we simulate hybrid mixtures by autoencoding individual stems with a neural codec. We compare two strategies combining AI-generated mix detection and source separation. A naive sequential pipeline, where source separation is followed by detection on separated sources, confirms that artifacts associated with an AI-generated stem are not reliably recovered by generic source separation systems. We therefore propose a parallel architecture in which source separation is only used to estimate source-relative energy within the mixture. We then train simple stem-specific binary classifiers that take as input the generated mix prediction together with the relative energy of the target stem on short audio chunks. Averaging chunk-level predictions yields encouraging track-level results, highlighting the potential of such approaches for detecting AI-generated stems in hybrid music.

Problem

• AI-generated stems
• hybrid human-AI music
• music detection
• source separation
• neural codec

Innovation

• AI-generated music detection
• hybrid human-AI music
• neural codec artifacts
• source separation
• stem-specific classification