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Machine-generated music (MGM) has become a powerful tool with applications in music therapy, personalised editing, and creative inspiration. However, its unregulated use threatens the entertainment, education, and arts sectors by diminishing the value of high-quality human compositions. Effective detection of machine-generated music (MGMD) is essential, yet progress is hindered by the lack of comprehensive datasets. To address this gap, we introduce M6, a large-scope benchmark dataset designed for MGMD research. M6 is distinguished by its diversity, encompassing multiple generators, domains, languages, cultural contexts, genres, and instruments, all provided in WAV format. We detail the data collection methodology and analysis, alongside baseline performance scores from foundational binary classification models, highlighting the complexity of MGMD and the need for further advancements. M6 serves as a robust resource to support future research in developing effective detection methods. The dataset is available at https://huggingface.co/datasets/yl7622/M6 to promote collaboration and innovation.
Li et al. (Fri,) studied this question.