M6: Multi-generator, Multi-domain, Multi-lingual and cultural, Multi-genres, Multi-instrument Machine-Generated Music Detection Databases

Machine-generated music (MGM) has emerged as a powerful tool with applications in music therapy, personalised editing, and creative inspiration for the music community. However, its unregulated use threatens the entertainment, education, and arts sectors by diminishing the value of high-quality human compositions. Detecting machine-generated music (MGMD) is, therefore, critical to safeguarding these domains, yet the field lacks comprehensive datasets to support meaningful progress. To address this gap, we introduce \textbf{M6}, a large-scale benchmark dataset tailored for MGMD research. M6 is distinguished by its diversity, encompassing multiple generators, domains, languages, cultural contexts, genres, and instruments. We outline our methodology for data selection and collection, accompanied by detailed data analysis, providing all WAV form of music. Additionally, we provide baseline performance scores using foundational binary classification models, illustrating the complexity of MGMD and the significant room for improvement. By offering a robust and multifaceted resource, we aim to empower future research to develop more effective detection methods for MGM. We believe M6 will serve as a critical step toward addressing this societal challenge. The dataset and code will be freely available to support open collaboration and innovation in this field.

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

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, the unregulated use of AI music generation (AIGM) tools 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.