Recognizing musical notation is an important skill to a full participation of Western classical music, but remains a largely under-researched topic in the psychology of music. One plausible reason of such omission is that, in the past, the research field has heavily relied on self-report of music reading ability, which was subjective and highly variable. This paper presents a reliable and valid tool for objectively measuring individual abilities in visual recognition of musical notation. The visual fluency task measures how fast one can accurately recognize a sequence of musical notation at a desired accuracy level using the adaptive psychometric method QUEST. We checked the reliability of this task in over 200 participants in terms of Guttman's λ-2 and Cronbach's alpha. Also, we evaluated the construct validity of this task by considering the convergent validity of this task with multiple external real-world measures of one's musical training background, with numerous experimental measures of visual tendencies of musical notation recognition and with sight-reading performance. Overall, the visual fluency task achieved satisfactory reliability and validity for measuring abilities in recognizing musical notation. This opens the door for characterizing the cognitive mechanisms, development, and individual differences in musical notation recognition, for understanding music learning and music psychology and for understanding of visual perceptual expertise in general.
The Javanese notation dataset comprises Javanese (song) notations for various instruments. This dataset includes 35 songs categorized into 7 song structures, which are similar to genres in modern music. Each song in this dataset includes the primary melody and notations for various instrument groups, including the instruments group ( and ), the and instruments, the instrument group, and the structural instruments group ( and ).
The relationship between music and emotion has been addressed within several disciplines, from more historico-philosophical and anthropological ones, such as musicology and ethnomusicology, to others that are traditionally more empirical and technological, such as psychology and computer science. Yet, understanding the link between music and emotion is limited by the scarce interconnections between these disciplines. Trying to narrow this gap, this data-driven exploratory study aims at assessing the relationship between linguistic, symbolic and acoustic features-extracted from lyrics, music notation and audio recordings-and perception of emotion.
When sight-reading a score, a timpanist needs to decide in real-time which stick to use to play a specific note while interpreting the musical material. Our main point of inquiry seeks to understand which sticking patterns performers employ and how they are affected by rhythmic stability. This paper analyzes the bi-manual sequencing (i.
All sufficiently large functional notations (ideographic or otherwise) are composites of discrete, structured elements (e.g., phonemes, morphemes, numerals).
