There has recently been a sharp increase in interest in Artificial Intelligence-Generated Content (AIGC). Despite this, musical components such as time signatures have not been studied sufficiently to form an algorithmic determination approach for new compositions, especially lyrical songs. This is likely because of the neglect of musical details, which is critical for constructing a robust framework. Specifically, time signatures establish the fundamental rhythmic structure for almost all aspects of a song, including the phrases and notes. In this paper, we propose a novel approach that only uses lyrics as input to automatically generate a fitting time signature for lyrical songs and uncover the latent rhythmic structure utilizing explainable machine learning models. In particular, we devise multiple methods that are associated with discovering lyrical patterns and creating new features that simultaneously contain lyrical, rhythmic, and statistical information. In this approach, the best of our experimental results reveal a 97.6% F1 score and a 0.996 Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) score. In conclusion, our research directly generates time signatures from lyrics automatically for new scores utilizing machine learning, which is an innovative idea that approaches an understudied component of musicology and therefore contributes significantly to the future of Artificial Intelligence (AI) music generation.

翻译：近年来，人工智能生成内容（AIGC）研究热度急剧上升。尽管如此，诸如节拍等音乐构成要素尚未得到充分研究以形成适用于新作品（尤其是歌词歌曲）的算法确定方法。这可能是由于对构建稳健框架至关重要的音乐细节的忽视。具体而言，节拍确立了歌曲几乎所有方面的基础节奏结构，包括短句和音符。本文提出了一种仅使用歌词作为输入的新型方法，可自动为歌词歌曲生成合适的节拍，并利用可解释机器学习模型揭示其隐式节奏结构。具体而言，我们设计了多种与发现歌词模式相关的方法，并创建了同时包含歌词特征、节奏特征和统计特征的新特征。通过该方法，最佳实验结果显示F1分数达到97.6%，受试者工作特征（ROC）曲线下面积（AUC）达到0.996。总之，本研究直接利用机器学习从歌词自动生成新乐谱的节拍，这一创新思路涉及了音乐学中尚未得到充分研究的领域，因此对人工智能（AI）音乐生成的未来发展具有重要贡献。