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      Real-Time Aural and Visual Feedback for Improving Violin Intonation

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          Abstract

          Playing with correct intonation is one of the major challenges for a string player. A player must learn how to physically reproduce a target pitch, but before that, the player must learn what correct intonation is. This requires audiation- the aural equivalent of visualization- of every note along with self-assessment whether the pitch played matches the target, and if not, what action should be taken to correct it. A challenge for successful learning is that much of it occurs during practice, typically without outside supervision. A student who has not yet learned to hear correct intonation may repeatedly practice out of tune, blithely normalizing bad habits and bad intonation. The real-time reflective nature of intonation and its consistent demand on attention make it a ripe target for technological intervention. Using a violin augmented to combine fingerboard sensors with audio analysis for real-time pitch detection, we examine the efficacy of three methods of real-time feedback for improving intonation and pitch learning. The first, aural feedback in the form of an in-tune guide pitch following the student in real-time, is inspired by the tradition of students playing along with teachers. The second is visual feedback on intonation correctness using an algorithm optimized for use throughout normal practice. The third is a combination of the two methods, simultaneously providing aural and visual feedback. Twelve beginning violinists, including children and adults, were given four in-situ 20–30 min lessons. Each lesson used one of the intonation feedback methods, along with a control lesson using no feedback. We collected data on intonation accuracy and conducted interviews on student experience and preference. The results varied by player, with evidence of some players being helped by the feedback methods but also cases where the feedback was distracting and intonation suffered. However interviews suggested a high level of interest and potential in having such tools to help during practice, and results also suggested that it takes time to learn to use the real-time aural and visual feedback. Both methods of feedback demonstrate potential for assisting self-reflection during individual practice.

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          Most cited references43

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          YIN, a fundamental frequency estimator for speech and music.

          An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds. It is based on the well-known autocorrelation method with a number of modifications that combine to prevent errors. The algorithm has several desirable features. Error rates are about three times lower than the best competing methods, as evaluated over a database of speech recorded together with a laryngograph signal. There is no upper limit on the frequency search range, so the algorithm is suited for high-pitched voices and music. The algorithm is relatively simple and may be implemented efficiently and with low latency, and it involves few parameters that must be tuned. It is based on a signal model (periodic signal) that may be extended in several ways to handle various forms of aperiodicity that occur in particular applications. Finally, interesting parallels may be drawn with models of auditory processing.
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            Following a discussion of the meaning of the term "skills" and a review of historical influences on their learning, a closed-loop theory for learning simple movements is presented. Empirical generalizations from the literature are stated, and the theory is used to explain them. The generalizations are of 2 classes: learning through the application of knowledge of results, and the effects of withdrawing knowledge of results.
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                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                02 April 2019
                2019
                : 10
                : 627
                Affiliations
                Augmented Instruments Laboratory, Centre for Digital Music, Queen Mary University of London, Electrical Engineering & Computer Science , London, United Kingdom
                Author notes

                Edited by: Alfonso Perez-Carrillo, Universidad Pompeu Fabra, Spain

                Reviewed by: Esther H. S. Mang, Hong Kong Baptist University, Hong Kong; Miguel Molina-Solana, Imperial College London, United Kingdom; George Waddell, Royal College of Music, United Kingdom

                *Correspondence: Laurel S. Pardue laurel.s.pardue@ 123456gmail.com

                This article was submitted to Performance Science, a section of the journal Frontiers in Psychology

                Article
                10.3389/fpsyg.2019.00627
                6455216
                e03a281d-e464-41bc-ba4a-4f6b964c416c
                Copyright © 2019 Pardue and McPherson.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 May 2018
                : 06 March 2019
                Page count
                Figures: 7, Tables: 6, Equations: 1, References: 57, Pages: 19, Words: 15460
                Funding
                Funded by: Engineering and Physical Sciences Research Council 10.13039/501100000266
                Award ID: EP/N005112/1
                Categories
                Psychology
                Original Research

                Clinical Psychology & Psychiatry
                violin,intonation,motor learning,pedagogy,real-time feedback,aural feedback,visual feedback

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