Abstract
Current music technologies can assist in the process of learning to play a musical instrument and provide objective measures for evaluating the improvement of music students in concrete music tasks. In this paper, we investigated the effects of a sound quality visual feedback system (SQVFS) in violin learning. In particular, we studied the EEG activity of a group of participants with no previous violin playing experience while they learned to produce a stable sound (regarding pitch, dynamics, and timbre) in order to find motor learning biomarkers in a music task. Eighteen subjects with no prior experience in violin playing were divided into two groups: participants in the first group (experimental group, N = 9) practiced with instructional videos and offline feedback from the SQVFS provided in alternation with their performance, while participants in a second group (control group, N = 9) practiced with the instructional videos only. A third group of violin experts (players with more than 6 years of experience) performed the same task for comparative purposes (N = 7). All participants were asked to perform 20 trials (4 blocks of 5 trials) consisting of a violin bowing exercise while their EEG activity and their produced sound was recorded. Significant sound quality improvements along the session were found in all participants with the exception of participants in the expert group. In addition, participants in the experimental group showed increased interest in the learning process and significant improvement after the second block not present in the control group. A significant correlation between the levels of frontal gamma band power and the sound improvement along the task was found in both the experimental and control group. This result is consistent with the temporal binding model which associates gamma band power with the role of integrating (binding) information processed in distributed cortical areas. Task complexity demands more cognitive resources, more binding and thus, gamma band power enhancement, which may be reduced as the demanded task begins to be automated as it is likely to be the case in both beginners groups.
Highlights
We have evaluated the effectiveness of using a sound quality visual feedback system (SQVFS) to improve the quality of sound produced by of novice violin players while their EEG activity and the violin sound they produced was recorded
The results of IGAE ranked the Sound instability descriptor which included aperiodicity as the most important one to differentiate between beginners and experts with a value of 0.668. It was followed by pitch instability with 0.599; dynamic instability with 0.577; Sound instability without aperiodicity with 0.549 and aperiodicity with 0.514
A Wilcoxon rank-sum test was performed for each audio descriptor comparing experts and beginners showing significant results (p < 0.00001 for all the descriptors)
Summary
The study was carried out in the recording studio located in the Information and Communication Technologies Engineering (ETIC) department of the Universitat Pompeu Fabra, Barcelona and included the participation of twenty-five right-handed subjects. Participants with no violin (or viola, double-bass or cello) experience were included in the beginner’s group. This last group, was randomly divided in two groups: the first group [BF; 6 male, 3 female; mean age: 27.57 (4.46); all of them were musicians with several years of experience, mean: 9 (5.07)] practiced with instructional videos and offline feedback from the SQVFS reflecting the quality of their produced sound, while the second group [BNF; 8 male, 1 female; mean age: 27.2 (2.28)] practiced with the instructional videos only. Participants were asked to minimize blinking and facial movements during the exercise to avoid artifacts in the EEG signal
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