Augmented Reality to Facilitate Learning of the Acoustic Guitar

[Abstract] Many people wishing to learn a musical instrument opt to learn using alternative or informal methods instead of the traditional Master–Apprentice model that requires a greater cognitive load. This paper presents an augmented reality (AR)-based application designed to teach and train guitar chords, with the novelty that it is also used to teach short melodies consisting of four chord transitions so that users have to change hand and finger positions. The app uses high-quality 3D models of an acoustic guitar and animated hand to indicate correct finger positions and the movements required when changing from one chord to another. To follow the animated instructions, the learner overlaps the 3D model onto the neck of the physical guitar and his or her own hand. A system usability scale (SUS) questionnaire was used to measure the usability of the application. A score of 82.0 was obtained, which is higher than the average of 68 points that indicates the application is good from a user experience perspective, thus satisfying the purpose for which it was created. Having analysed the data for both groups—individuals with no prior experience of playing a musical instrument versus individuals with prior experience—it was concluded that the application provided a useful learning approach for all participants involved in the study, regardless of experience. That said, those possessing prior experience of playing an instrument learnt faster. It should be noted that the research revealed significant difference in learning by gender, with male participants learning faster than female participants. Similar results have been detected in other research performed in the field of music, as well as in other fields. As this study required spatial reasoning when viewing the 3D model, the differences identified this case may well have arisen as a consequence of differences in men and women’s spatial awareness, thereby leaving open an alternative line of research

Characterizing Movement Fluency in Musical Performance: Toward a Generic Measure for Technology Enhanced Learning

Virtuosity in music performance is often associated with fast, precise, and efficient sound-producing movements. The generation of such highly skilled movements involves complex joint and muscle control by the central nervous system, and depends on the ability to anticipate, segment, and coarticulate motor elements, all within the biomechanical constraints of the human body. When successful, such motor skill should lead to what we characterize as fluency in musical performance. Detecting typical features of fluency could be very useful for technology-enhanced learning systems, assisting and supporting students during their individual practice sessions by giving feedback and helping them to adopt sustainable movement patterns. In this study, we propose to assess fluency in musical performance as the ability to smoothly and efficiently coordinate while accurately performing slow, transitionary, and rapid movements. To this end, the movements of three cello players and three drummers at different levels of skill were recorded with an optical motion capture system, while a wireless electromyography (EMG) system recorded the corresponding muscle activity from relevant landmarks. We analyzed the kinematic and coarticulation characteristics of these recordings separately and then propose a combined model of fluency in musical performance predicting music sophistication. Results suggest that expert performers' movements are characterized by consistently smooth strokes and scaling of muscle phasic coactivation. The explored model of fluency as a function of movement smoothness and coarticulation patterns was shown to be limited by the sample size, but it serves as a proof of concept. Results from this study show the potential of a technology-enhanced objective measure of fluency in musical performance, which could lead to improved practices for aspiring musicians, instructors, and researchers

Enhancing music learning with smart technologies

Comunicació presentada a: 5th International Conference on Movement and Computing, celebrat del 28 al 30 de juny de 2018 a Gènova, Itàlia.Learning to play a musical instrument is a difficult task, requiring the development of sophisticated skills. Nowadays, such a learning process is mostly based on the master-apprentice model. Technologies are rarely employed and are usually restricted to audio and video recording and playback. The TELMI (Technology Enhanced Learning of Musical Instrument Performance) Project seeks to design and implement new interaction paradigms for music learning and training based on state-of-the-art multimodal (audio, image, video, and motion) technologies. Figure 1: Analysis of coordination, applying RecurrenceQuantification Analysis to the kinetic energy of the right wrist. Figure 2: A sample screen-shot of the intonation feedback on Piano Roll Mode. Figure 3: A sample screen-shot of the intonation feedback on Score View Mode. The project focuses on the violin as a case study. This practice work is intended as demo, showing to MOCO attendants the results the project obtained along two years of work. The demo simulates a setup at a higher education music institution, where attendants with any level of previous violin experience (and even with no experience at all) are invited to try the technologies themselves, performing basic tests of violin skill and pre-defined exercises under the guidance of the researchers involved in the project