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 systematic evaluation 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 systematic evaluation 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 Artificial 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 systematic evaluation results and their interpretability, we propose several directions to inspire future research to develop more robust and effective detection methods for MGM. We provide our codes and some samples on Github repository.

翻译：机器生成音乐作为一项突破性创新，已在音乐治疗、个性化编辑、创意灵感等音乐产业领域展现出广泛应用前景。然而，机器生成音乐的无序发展可能削弱高质量人类创作的价值，给娱乐、教育及艺术领域带来显著挑战。因此，机器生成音乐检测对于维护这些领域的完整性至关重要。尽管其意义重大，该领域目前仍缺乏推动实质性进展所需的系统性评估结果。为弥补这一空白，我们基于现有大规模数据集，采用多种音频处理基础模型开展实验，建立针对机器生成音乐检测任务的系统评估体系。所选模型涵盖传统机器学习模型、深度神经网络、基于Transformer的架构及状态空间模型。鉴于音乐本身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。除基础二分类结果外，我们运用多项可解释人工智能工具深入分析模型行为，揭示其决策机制。研究显示，ResNet18在域内与跨域测试中表现最优。通过系统评估结果及其可解释性的全面比较，我们提出若干发展方向，以激励未来研究构建更鲁棒高效的机器生成音乐检测方法。相关代码及部分样本已发布于GitHub仓库。