Key research themes
1. How can computational and machine learning methods advance the understanding and execution of structured musical analysis?
This research area focuses on leveraging computational models, particularly machine learning algorithms, to enhance the precision and objectivity in music analysis methodologies such as Schenkerian analysis. By quantifying the significance of various musical parameters and modeling hierarchical structures, these studies aim to reveal implicit cognitive processes supporting expert musical interpretation, enabling reproducible and insightful analytical outcomes beyond subjective intuition.
2. What roles do performance characteristics play in shaping musical perception distinct from compositional structure?
This theme investigates how aspects of music performance—such as timing, dynamics, timbre, and interpretative expression—affect listeners' experience independently from the underlying compositional text or score. Research emphasizes empirical analysis of recorded performances to characterize subtle expressive variations, their perception, and the implications for music cognition and Information Retrieval systems. Understanding performers' contributions enhances interpretations of music beyond notated intentions.
3. How do cultural, historical, and sociological contexts inform the understanding and evolution of musical style, genre, and tradition?
This area delves into how musical meaning, stylistic identity, and genre conventions are constructed and transmitted within specific cultural and historical frameworks. It includes the examination of cross-cultural syntheses, music’s sociopolitical dimensions, and tradition reformulations. By integrating ethnomusicological, historical, and postcolonial perspectives, these studies reveal how music reflects and shapes social identities and evolves through intercultural dialogue, technology, and collective memory.