Many practices have been presented in music generation recently. While stylistic music generation using deep learning techniques has became the main stream, these models still struggle to generate music with high musicality, different levels of music structure, and controllability. In addition, more application scenarios such as music therapy require imitating more specific musical styles from a few given music examples, rather than capturing the overall genre style of a large data corpus. To address requirements that challenge current deep learning methods, we propose a statistical machine learning model that is able to capture and imitate the structure, melody, chord, and bass style from a given example seed song. An evaluation using 10 pop songs shows that our new representations and methods are able to create high-quality stylistic music that is similar to a given input song. We also discuss potential uses of our approach in music evaluation and music therapy.

最近在音乐生成中出现了许多实践。虽然使用深度学习技术的风格音乐生成已成为主流，但这些模型仍然难以生成具有高音乐性、不同层次的音乐结构和可控性的音乐。此外，音乐治疗等更多应用场景需要从少数给定的音乐示例中模仿更具体的音乐风格，而不是捕捉大数据语料库的整体流派风格。为了解决挑战当前深度学习方法的要求，我们提出了一种统计机器学习模型，能够捕获和模仿给定示例种子歌曲的结构、旋律、和弦和低音风格。使用 10 首流行歌曲进行的评估表明，我们的新表示和方法能够创建与给定输入歌曲相似的高质量风格音乐。我们还讨论了我们的方法在音乐评估和音乐治疗中的潜在用途。