Automatic Time Signature Determination for New Scores Using Lyrics for Latent Rhythmic Structure

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.

Multimodal Lyrics-Rhythm Matching

Despite the recent increase in research on artificial intelligence for music, prominent correlations between key components of lyrics and rhythm such as keywords, stressed syllables, and strong beats are not frequently studied. Ths is likely due to challenges such as audio misalignment, inaccuracies in syllabic identification, and most importantly, the need for cross-disciplinary knowledge. To address this lack of research, we propose a novel multimodal lyrics-rhythm matching approach in this paper that specifically matches key components of lyrics and music with each other without any language limitations. We use audio instead of sheet music with readily available metadata, which creates more challenges yet increases the application flexibility of our method. Furthermore, our approach creatively generates several patterns involving various multimodalities, including music strong beats, lyrical syllables, auditory changes in a singer's pronunciation, and especially lyrical keywords, which are utilized for matching key lyrical elements with key rhythmic elements. This advantageous approach not only provides a unique way to study auditory lyrics-rhythm correlations including efficient rhythm-based audio alignment algorithms, but also bridges computational linguistics with music as well as music cognition. Our experimental results reveal an 0.81 probability of matching on average, and around 30% of the songs have a probability of 0.9 or higher of keywords landing on strong beats, including 12% of the songs with a perfect landing. Also, the similarity metrics are used to evaluate the correlation between lyrics and rhythm. It shows that nearly 50% of the songs have 0.70 similarity or higher. In conclusion, our approach contributes significantly to the lyrics-rhythm relationship by computationally unveiling insightful correlations.