缅北强奸

The Doctoral Colloquium is open to all.

Doctoral Colloquium: Alberto Acquilino,聽PhD candidate, 缅北强奸, Music Technology

罢颈迟濒别:听Real-Time Computational Feedback Systems for Enriched Classical Music Training

础产蝉迟谤补肠迟:听Learning to play an instrument and performing in music ensembles offers multiple benefits from social, emotional, cognitive and health perspectives. The relationship between music studies and improved socio-emotional learning are widely discussed and empirically demonstrated. Benefits include improved academic achievements, self-regulation, social cohesion and inclusion, empathy and emotional intelligence, creativity and psychological wellbeing, just to name a few. However, mastering a musical instrument to a level sufficient to enjoy and play in ensemble settings is challenging: it requires complex coordination of multiple movements, balancing physical and mental endurance while communicating emotions and abstract thoughts.

The training of musicians bears strong similarities to that of sports athletes: the pursuit of movement coordination to maximize muscular efficiency applies as much to the tennis player in hitting the ball into the opponent's court as it does to the music player in achieving proper technique. Despite this, musicians often lack clear visualization of their proficiency in sound production, compounded by the scarcity of accessible technological tools that effectively support music pedagogy. Common tools like metronomes and tuners help musicians play in tempo and in tune, but there are no equivalent devices for refining and controlling other critical musical skills - such as sound color (timbre) and articulation (attack) - which are essential for effectively expressing musical ideas. The complexity of these techniques often leads to misunderstandings, improper practice habits, and significant difficulties in learning, resulting in anxiety, frustration, injury, and even dropout before students can attain the benefits mentioned above.

This research explores the role of low-cost interactive technologies in teaching musical techniques in an efficient and healthy way, with a particular focus on variable pitch instruments (e.g., winds and bowed strings), using the trumpet as a reference. The research is grounded in the Western classical music tradition, as taught in conservatories and music schools. By primarily analyzing audio information from the musician鈥檚 performance, the proposed tools provide real-time aural and visual feedback designed to enrich music education. Mobile app technologies are being developed and tested with students to validate hypotheses and establish guidelines for future innovations in the field.

叠颈辞驳谤补辫丑测:听Alberto Acquilino is a PhD candidate and Tomlinson Scholar at 缅北强奸, conducting research at the Computational Acoustic Modeling Laboratory. His work centers on the development of computational technologies aimed at enriching classical music pedagogy. Specifically, he explores the intersection of music education, technology, health, and accessibility, with a focus on designing real-time feedback systems for variable pitch instruments. Alberto co-founded the Technology Enhanced Music Education Symposium 2024 in Montreal and serves on the academic board of the Global Leaders Institute for Arts Innovation, where he leads a course on Music and AI. He is also a mentor for the Google Summer of Code, collaborating with the International Neuroinformatics Coordinating Facility. Alberto holds Master鈥檚 degrees in music performance and mechanical engineering from universities in Italy and France.