Machine-generated music (MGM) has become a groundbreaking innovation with wide-ranging applications, such as music therapy, personalised editing, and creative inspiration within the music industry. However, the unregulated proliferation of MGM presents considerable challenges to the entertainment, education, and arts sectors by potentially undermining the value of high-quality human compositions. Consequently, MGM detection (MGMD) is crucial for preserving the integrity of these fields. Despite its significance, MGMD domain lacks comprehensive benchmark results necessary to drive meaningful progress. To address this gap, we conduct experiments on existing large-scale datasets using a range of foundational models for audio processing, establishing benchmark results tailored to the MGMD task. Our selection includes traditional machine learning models, deep neural networks, Transformer-based architectures, and State Space Models (SSM). Recognising the inherently multimodal nature of music, which integrates both melody and lyrics, we also explore fundamental multimodal models in our experiments. Beyond providing basic binary classification outcomes, we delve deeper into model behaviour using multiple explainable Aritificial Intelligence (XAI) tools, offering insights into their decision-making processes. Our analysis reveals that ResNet18 performs the best according to in-domain and out-of-domain tests. By providing a comprehensive comparison of benchmark results and their interpretability, we propose several directions to inspire future research to develop more robust and effective detection methods for MGM.

翻译：机器生成音乐（MGM）已成为一项具有广泛应用的突破性创新，例如音乐治疗、个性化编辑以及音乐产业内的创意灵感。然而，MGM的无序扩散通过潜在削弱高质量人类作品的价值，给娱乐、教育和艺术领域带来了巨大挑战。因此，MGM检测（MGMD）对于维护这些领域的完整性至关重要。尽管意义重大，MGMD领域仍缺乏推动实质性进展所需的全面基准结果。为弥补这一空白，我们利用一系列音频处理基础模型在现有大规模数据集上进行了实验，建立了针对MGMD任务的基准结果。我们的选择包括传统机器学习模型、深度神经网络、基于Transformer的架构以及状态空间模型（SSM）。认识到音乐本质上融合旋律与歌词的多模态特性，我们在实验中也探索了基础多模态模型。除了提供基本的二元分类结果外，我们还使用多种可解释人工智能（XAI）工具深入探究模型行为，揭示其决策过程。我们的分析表明，根据域内和域外测试，ResNet18表现最佳。通过提供基准结果及其可解释性的全面比较，我们提出了若干方向以启发未来研究，从而开发更鲁棒有效的MGM检测方法。