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Practice Support for Violin Bowing by Measuring Bow Pressure and Position

Yurina Mizuho, Yuta Sugiura

TL;DR

This paper tackles the challenge of training violin bowing by focusing on bow pressure, a less visible but crucial parameter for tonal quality. It first quantifies differences between experienced players and beginners using a load cell under the bridge and motion capture for bow-position data, revealing that experts maintain higher and more consistent bow pressure and execute smoother direction changes. Building on these insights, the authors develop a visual feedback system that guides beginners to apply sufficient and uniform bow pressure, and evaluate its effectiveness through a two-day training study with and without feedback. Results show that explicit comparison with expert patterns and real-time feedback can improve pressure magnitude and consistency, with audible quality improving after practice, though user experience and cognitive load pose design considerations for practical deployment.

Abstract

The violin is one of the most popular musical instruments. Various parameters of bowing motion, such as pressure, position, and speed, are crucial for producing a beautiful tone. However, mastering them is challenging and requires extensive practice. In this study, we aimed to support practice of bowing, focusing on bow pressure. First, we compared the bowing movements, specifically bow pressure, bow position, and bow speed, of eight experienced players with those of eight beginners. Next, we developed and evaluated a visual feedback system that displays bow pressure to support practice. We taught the identified differences to 14 beginners, dividing them into two groups: one practiced with an explanation, and the other with both an explanation and a feedback system. These two experiments found that clarifying the characteristics unique to experienced players can support practice.

Practice Support for Violin Bowing by Measuring Bow Pressure and Position

TL;DR

This paper tackles the challenge of training violin bowing by focusing on bow pressure, a less visible but crucial parameter for tonal quality. It first quantifies differences between experienced players and beginners using a load cell under the bridge and motion capture for bow-position data, revealing that experts maintain higher and more consistent bow pressure and execute smoother direction changes. Building on these insights, the authors develop a visual feedback system that guides beginners to apply sufficient and uniform bow pressure, and evaluate its effectiveness through a two-day training study with and without feedback. Results show that explicit comparison with expert patterns and real-time feedback can improve pressure magnitude and consistency, with audible quality improving after practice, though user experience and cognitive load pose design considerations for practical deployment.

Abstract

The violin is one of the most popular musical instruments. Various parameters of bowing motion, such as pressure, position, and speed, are crucial for producing a beautiful tone. However, mastering them is challenging and requires extensive practice. In this study, we aimed to support practice of bowing, focusing on bow pressure. First, we compared the bowing movements, specifically bow pressure, bow position, and bow speed, of eight experienced players with those of eight beginners. Next, we developed and evaluated a visual feedback system that displays bow pressure to support practice. We taught the identified differences to 14 beginners, dividing them into two groups: one practiced with an explanation, and the other with both an explanation and a feedback system. These two experiments found that clarifying the characteristics unique to experienced players can support practice.
Paper Structure (26 sections, 13 figures, 1 table)

This paper contains 26 sections, 13 figures, 1 table.

Figures (13)

  • Figure 1: Bow pressure and position measurement
  • Figure 2: Load cell installed under the bridge
  • Figure 3: Position of optical markers attached to the violin
  • Figure 4: Time series data of bow pressure and position
  • Figure 5: Relationship between bow position and pressure (*: p$<$0.05)
  • ...and 8 more figures