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Area of Science:

  • Music Technology
  • Machine Learning
  • Acoustics

Background:

  • Improving self-practice is crucial for musical instrument learning.
  • Existing practice assistance systems often rely on intrusive sensors.
  • Wind instruments present unique challenges for sensor-based parameter acquisition.

Purpose of the Study:

  • To develop a sensorless system for estimating wind instrument control parameters.
  • To leverage machine learning for analyzing instrument sound.
  • To enhance musical instrument practice through accurate performance feedback.

Main Methods:

  • A machine learning framework was employed for parameter estimation.
  • A robotic performer generated extensive training data with precise control parameters.
  • Human performance data was integrated to refine the model.

Main Results:

  • The system accurately estimated control parameters for novice flute players.
  • Spearman's rank correlation coefficient validated high estimation accuracy.
  • The sensorless approach proved effective for wind instruments.

Conclusions:

  • The proposed sensorless system offers a viable solution for wind instrument practice analysis.
  • Machine learning analysis of sound can effectively replace physical sensors.
  • This technology has the potential to significantly improve music education and practice.