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Bowing Gestures Classification in Violin Performance: A Machine Learning Approach.

David Dalmazzo1, Rafael Ramírez1

  • 1Music Technology Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

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|March 20, 2019
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Summary
This summary is machine-generated.

This study introduces a machine learning model for automatic violin bow technique classification using motion data. The Hierarchical Hidden Markov Model (HHMM) achieves over 94% accuracy, aiding music education.

Keywords:
Hidden Markov ModelIMUaudio descriptorsbow strokesbraceletmachine learningsensorstechnology enhanced learning

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Area of Science:

  • Music Technology
  • Machine Learning in Music Performance
  • Biomechanics of Musical Instruments

Background:

  • Musician gestures are crucial for sound production and expressiveness.
  • Advanced motion capture technology enables detailed analysis of body movements.
  • Accurate classification of violin bowing techniques is essential for performance and pedagogy.

Purpose of the Study:

  • To develop a machine learning approach for automatic classification of violin bow gestures.
  • To investigate the effectiveness of Hierarchical Hidden Markov Models (HHMM) using motion and audio data.
  • To create a system for real-time feedback in violin learning.

Main Methods:

  • Recorded motion data from a professional violinist using a Myo device (forearm inertial data).
  • Synchronized motion data with audio recordings of seven distinct bowing techniques.
  • Extracted features from motion and audio data, then trained an HHMM for classification.

Main Results:

  • The HHMM model achieved over 94% accuracy in identifying the seven studied bowing techniques.
  • Demonstrated the feasibility of using motion data for automated gesture recognition in violin playing.
  • The system successfully distinguished between techniques like Détaché, Martelé, and Spiccato.

Conclusions:

  • Machine learning, specifically HHMM, is highly effective for automatic violin bow gesture classification.
  • The developed system offers potential for practical application in music education and practice.
  • Real-time feedback based on accurate gesture recognition can significantly benefit violin students.