The patterns are statistically analyzed in this empirical study. These practice patterns are coined by the authors and are explained in detail in Section 5. We show that the MPB framework allows us to clearly observe important practice behaviors, including Drill-Smooth and Drill-Correct, a Memorization Strategy, Review and Explore behavior, and Expressive Evolution. In order to test the efficacy of the proposed visualizations, the MPB framework is tested on recorded practice sessions of beginner, intermediate, and expert piano learning students. MPB offers novel Practice Session Work Maps of entire practice sessions and Practice Segment Arcs to allow music teachers and learners to visually assess practice strategies to overcome specific difficulties. Such interfaces extend traditional teaching techniques, but do not provide an overview of whole practice sessions. The Expression Synthesis Project (ESP) interface uses an automobile driving (wheel and pedals) to modulate tempo and loudness (Chew et al., 2005), with roadmaps acting as guides to expressive performance (Chew et al., 2006). In the Jump'n'Rhythm (Alexandrovsky et al., 2016) video game, the user has to make a virtual character jump in time to rhythm patterns. Some use gamification and traveling metaphors to engage users to think about music performance. Other interfaces have introduced visual feedback to help music learners correct errors in pitch accuracy, for example, in singing (Huang and Chu, 2016). Music tutoring applications such as iScore (Upitis et al., 2012) and PRAISE 4 offer human tutoring support and assessment through online graphical user interfaces tutors can annotate user-provided recordings and send feedback to students to guide further practice. To fill this gap, we introduce the Music Practice Browser (MPB), which allows users to monitor the development of expressivity in music practice.
These applications focus on improving students' technical abilities, but lack the capability of monitoring the development of expressivity, a defining aspect of musical performance.
#SONIC VISUALISER CHORD DIAGRAM SOFTWARE#
Some music education software also track time spent on practizing a piece.
Software applications such as Wolfie, 1 Superscore, 2 and BandPad 3 score follow students whilst they play and mark notes played as correct or incorrect. Recent advances in music computing techniques and music data capture have enable parts of music learning to be supported by personal computers and smart mobile devices. The analysis reveals practice patterns and behavior differences between beginners and experts, such as a higher proportion of Drill-Smooth patterns in expert practice. The practice patterns found include Drill-Correct, Drill-Smooth, Memorization Strategy, Review and Explore, and Expressive Evolution. We then test the new system on practice sessions of pianists of varying levels of expertise ranging from novice to expert. The system takes beat and practice segment information together with a musical score in XML format as input, and produces a number of different visualizations: Practice Session Work Maps give an overview of contiguous practice segments Practice Segment Arcs make evident transitions and repeated segments Practice Session Precision Maps facilitate the identifying of errors Tempo-Loudness Evolution Graphs track expressive variations over the course of a practice session. The Music Practice Browser provides a graphical interface for reviewing recorded practice sessions, which allows musicians, teachers, and researchers to examine aspects and features of music practice behaviors. To bridge this gap, we present a novel visualization system, the Music Practice Browser, for representing, identifying, and analysing music practice behaviors. Practice is an essential part of music training, but critical content-based analyses of practice behaviors still lack tools for conveying informative representation of practice sessions.
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