# Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy
## 2 Related Works  
### Structure Retrieval in MIR  
The field of MIR has witnessed a growing interest in understanding the structural organization of music. Numerous studies have explored different approaches for extracting structural information from music scores and audio recordings. Early research focused on similarity-based methods to identify recurring patterns and repetitions within music. These approaches, often utilizing techniques like dynamic programming or hierarchical clustering, compared segments based on melodic, harmonic, or rhythmic similarities [19; 21; 22]. While these methods provided valuable insights into music structure, they primarily focused on surface-level similarities and might not fully capture the intricate hierarchical relationships present in music. More recently, researchers have explored symbolic music data as a more concise and interpretable source for structural analysis. This approach allows for a deeper examination of the musical hierarchy, capturing both macro and micro-level structures without the ambiguities introduced by audio signal processing. Notably, studies by [PERSON] and [PERSON] proposed a hierarchical model of music structure based on grouping principles and time-span reduction, laying the foundation for understanding the hierarchical organization of musical elements [10]. [PERSON] et al. further investigated the processing of hierarchical syntactic structure in music, highlighting the cognitive significance of hierarchical organization [11]. Several research efforts have focused on developing computational models for extracting hierarchical music structures from symbolic data. [PERSON] et al. (2014) proposed a compositional hierarchical model for MIR [14], while [PERSON] et al. (2022) explored multi-level representations for hierarchical music structure analysis [17]. [PERSON] et al. (2024) examined the interconnectedness of music structures with other musical elements like harmony, melody, and rhythm, emphasizing the importance of a holistic approach to structure analysis [16]. These studies highlight the growing interest in developing robust computational models for detailed structural analysis directly on symbolic music data. This approach promises more precise insights into the musical hierarchy, paving the way for advancements in MIR applications like automated composition, music recommendation systems, and music theory education. It is important to note that the field of music theory provides a rich theoretical framework for understanding music structure, with concepts like form, theme, and variation playing crucial roles in defining musical coherence. [PERSON] (2024) [27] proposes a mathematical foundation for music theory and composition, exploring the concept of \"structure\" as a fundamental element in music analysis. [PERSON] et al. (2025) [28] proposed MusicAOG, an energy-based hierarchical and/or graph model that enables interpretable learning and controllable generation of symbolic music through Metropolis-Hastings sampling, though requiring expert annotations and simplified evaluations. While significant progress has been made in the field of music structure retrieval, there is still a need for further research in developing accurate and efficient models that can effectively capture the intricate hierarchical relationships present in music. This research will contribute to a deeper understanding of music structure and enable the development of more sophisticated MIR applications. In summary, Table 1 summarizes the literature on music structure analysis, highlighting the methods employed and their limitations. The table illustrates the diversity of approaches in the field, ranging from dynamic programming and clustering to cognitive syntax analysis and compositional hierarchy. However, many studies face limitations such as surface-level similarity, non-computational frameworks, and genre-specific features.