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.
The production of good sound generation in the violin is a complex task that requires coordination and spatiotemporal control of bowing gestures. The use of motion-capture technologies to improve performance or reduce injury risks in the area of kinesiology is becoming widespread. The combination of motion accuracy and sound quality feedback has the potential of becoming an important aid in violin learning. In this study, we evaluate motion-capture and sound-quality analysis technologies developed inside the context of the TELMI, a technology-enhanced music learning project. We analyzed the sound and bow motion of 50 participants with no prior violin experience while learning to produce a stable sound in the violin. Participants were divided into two groups: the experimental group (N = 24) received real-time visual feedback both on kinematics and sound quality, while participants in the control group (N = 26) practiced without any type of external help. An additional third group of violin experts performed the same task for comparative purposes (N = 15). After the practice session, all groups were evaluated in a transfer phase without feedback. At the practice phase, the experimental group improved their bowing kinematics in comparison to the control group, but this was at the expense of impairing the sound quality of their performance. At the retention phase, the experimental group showed better results in sound quality, especially concerning control of sound dynamics. Besides, we found that the expert group improved the stability of their sound while using the technology. All in all, these results emphasize the importance of feedback technologies in learning complex tasks, such as musical instrument learning.
Auditory-guided vocal learning is a mechanism that operates both in humans and other animal species making us capable to imitate arbitrary sounds. Both auditory memories and auditory feedback interact to guide vocal learning. This may explain why it is easier for humans to imitate the pitch of a human voice than the pitch of a synthesized sound. In this study, we compared the effects of two different feedback modalities in learning pitch-matching abilities using a synthesized pure tone in 47 participants with no prior music experience. Participants were divided into three groups: a feedback group (N = 15) receiving real-time visual feedback of their pitch as well as knowledge of results; an equal-timbre group (N = 17) receiving additional auditory feedback of the target note with a similar timbre to the instrument being used (i.e., violin or human voice); and a control group (N = 15) practicing without any feedback or knowledge of results. An additional fourth group of violin experts performed the same task for comparative purposes (N = 15). All groups were posteriorly evaluated in a transfer phase. Both experimental groups (i.e., the feedback and equal-timbre groups) improved their intonation abilities with the synthesized sound after receiving feedback. Participants from the equal-timber group seemed as capable as the feedback group of producing the required pitch with the voice after listening to the human voice, but not with the violin (although they also showed improvement). In addition, only participants receiving real-time visual feedback learned and retained in the transfer phase the mapping between the synthesized pitch and its correspondence with the produced vocal or violin pitch. It is suggested that both the effect of an objective external reward, together with the experience of exploring the pitch space with their instrument in an explicit manner, helped participants to understand how to control their pitch production, strengthening their schemas, and favoring retention.
We present a study of the e ects of feedback technologies on the learning process of novice violin students. Twenty-one subjects participated in our experiment, divided into two groups: Beginners (participants with no prior violin playing experience, N=14), and experts (participants with more than 6 years of violin playing experience, N=7). e beginners group was further divided into two: a group of beginners learning with Youtube videos (N=7), and a group of beginners with additional feedback related to the quality of their performance (N=7). Participants were asked to perform a violin exercise during 21 trials while their audio was recorded and analyzed. ree di erent audio descriptors were extracted from each audio in order to evaluate the quality of the performance: Dynamic stability, pitch stability and aperiodicity. Beginners showed a signi cant improvement during the session(i.e. by comparing the beginning and the end of the session)in the quality of the sound recorded, while experts maintained their results. However, only the beginner group with feedback showed signi cant improvement between the middle and late part of the session, while the group without feedback remained stable.
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