Bridge maintenance and safety are essential for transportation authorities, and Non-Destructive Evaluation (NDE) techniques are critical to assessing structural integrity. However, interpreting NDE data can be time-consuming and requires expertise, potentially delaying decision-making. Recent advancements in Large Language Models (LLMs) offer new ways to automate and improve this analysis. This pilot study introduces a holistic assessment of LLM capabilities for interpreting NDE contour maps and demonstrates the effectiveness of LLMs in providing detailed bridge condition analyses. It establishes a framework for integrating LLMs into bridge inspection workflows, indicating that LLM-assisted analysis can enhance efficiency without compromising accuracy. In this study, several state-of-the-art LLMs are explored with prompts specifically designed to enhance the quality of image descriptions, which are applied to interpret five different NDE contour maps obtained through technologies such as Ground Penetrating Radar (GPR), Electrical Resistivity (ER), Impact-Echo (IE), and Ultrasonic Surface Waves (USW) for assessing bridge conditions. Each LLM model is evaluated based on its ability to produce detailed descriptions, identify defects, provide actionable recommendations, and demonstrate overall accuracy. The research indicates that the ChatGPT-4, Claude 3.5 Sonnet, CogVLM2, and ShareGPT4V models provide better image descriptions, effectively covering a wide range of topics related to the bridge’s condition. The outputs from these four models are subsequently summarized using five different LLMs to form a comprehensive overview of the bridge. Notably, LLMs ChatGPT-4 and Claude 3.5 Sonnet generate more detailed and effective summaries. The findings suggest that LLMs have the potential to significantly improve the efficiency and accuracy of infrastructure evaluation processes. This pilot study presents an innovative approach that leverages LLMs for image captioning in parallel and summarization, enabling faster decision-making in bridge maintenance and enhancing infrastructure management and safety assessments.

Artificial Intelligence (AI) song generation has emerged as a popular topic, yet the focus on exploring the latent correlations between specific lyrical and rhythmic features remains limited. In contrast, this pilot study particularly investigates the relationships between keywords and rhythmically stressed features such as strong beats in songs. It focuses on several key elements: keywords or non-keywords, stressed or unstressed syllables, and strong or weak beats, with the aim of uncovering insightful correlations. Experimental results indicate that, on average, 80.8% of keywords land on strong beats, whereas 62% of non-keywords fall on weak beats. The relationship between stressed syllables and strong or weak beats is weak, revealing that keywords have the strongest relationships with strong beats. Additionally, the lyrics-rhythm matching score, a key matching metric measuring keywords on strong beats and non-keywords on weak beats across various time signatures, is 0.765, while the matching score for syllable types is 0.495. This study demonstrates that word types strongly align with their corresponding beat types, as evidenced by the distinct patterns, whereas syllable types exhibit a much weaker alignment. This disparity underscores the greater reliability of word types in capturing rhythmic structures in music, highlighting their crucial role in effective rhythmic matching and analysis. We also conclude that keywords that consistently align with strong beats are more reliable indicators of lyrics-rhythm associations, providing valuable insights for AI-driven song generation through enhanced structural analysis. Furthermore, our development of tailored Lyrics-Rhythm Matching (LRM) metrics maximizes lyrical alignments with corresponding beat stresses, and our novel LRM file format captures critical lyrical and rhythmic information without needing original sheet music.

This is a song completely generated automatically by AI from lyrics using purely novel algorithms we proposed and does not require any form of training data. It follows the music theory conventions and guidelines. The algorithm selects from the most common time signatures in songs such as 2/4, 3/4, and 4/4, and does not provide solely a uniform rhythm but a variety of rhythms instead. Since the song utilized as a demonstration is generated from lyrics written by a child and for children, the rhythms are relatively simple. The melody is controlled within an interval from C4 to C5 so that it includes all of the notes within an octave and is also “singable” for the majority of the human population for at least a portion of the interval. Additionally, the algorithm can be used to generate multiple songs for one set of lyrics. The demonstrated song is designed to be sung by children due to its easy, comprehensible nature and simple melody. The tempo is manually set at 120 beats per minute and the time signature is automatically determined as 4/4 time by our algorithms.

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.

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. This 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.