From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview

TL;DR

This paper reviews AI-generated music detection methods, explores how audio deepfake detection techniques can be adapted for music, and discusses future research directions to address challenges in the field.

Contribution

It provides a comprehensive overview of AIGM detection, proposes leveraging foundation models from deepfake audio detection, and discusses future research pathways.

Findings

Overview of existing AIGM detection methods

Proposal to adapt deepfake audio detection techniques for music

Discussion of future challenges and research directions

Abstract

As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an art form and medium for entertainment, deeply rooted into human culture, is seeing an increased involvement of AI into its production. However, despite the effective application of AI music generation (AIGM) tools, the unregulated use of them raises concerns about potential negative impacts on the music industry, copyright and artistic integrity, underscoring the importance of effective AIGM detection. This paper provides an overview of existing AIGM detection methods. To lay a foundation to the general workings and challenges of AIGM detection, we first review general principles of AIGM, including recent advancements in deepfake audios, as well as multimodal detection techniques. We further propose a potential pathway for leveraging foundation models from audio deepfake detection to AIGM detection. Additionally, we discuss implications of these tools and propose directions for future research to address ongoing challenges in the field.