Novel Sensing Technology and Networks for Music Learning and Education

Dear Colleagues,

Learning how to play a musical instrument is a long process that requests several hours of training guided by experienced instructors. Several repetitions of simple exercises are required to learn how to interact with the instrument in a natural, fluid, and smooth way. Such a long process is necessary to bring the sensory motor control to a professional level.

Smart interfaces embedded into everyday objects or directly worn by the users are becoming increasingly relevant to augment, train, or simply assess sensorimotor functions in several application scenarios, even in music. While there are several examples of the application of smart interfaces and software to mimic a musical instrument and increase the possibilities of training, few devices are available for functional assessment of musicians to provide them with additional feedbacks on the quality of their gestures and on how they interact with the real instrument.

Nowadays, several technologies may be used to address this issue: the arsenal of available ammunitions ranges from smart instrumented objects to wearable networks of sensors. These sensing technologies make possible recording interactions with instruments as well as monitoring physiological and biomechanical signals on-field, with improved capability to quantify behaviors at a more fine-grained space-time scale.

This Special Issue of Sensors aims to collect current developments in the design, characterization, and validation of novel sensing technologies and networks for the assessment of instrumental music learning.

Original studies and review papers from human-centered technology, bioengineering, music education, systematic musicology, artificial intelligence and IoT, neuroscience, and other related fields will be considered.

Dr. Fabrizio Taffoni
Prof. Dr. Domenico Formica
Dr. Nicola Di Stefano
Prof. Dr. Marc Leman
Guest Editors

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• human-centered technology
• technology-enhanced music learning
• music education
• smart devices
• wearables
• functional assessment
• tool use
• body area networks
• motor learning
• motor control