Harnessing Machine Learning in Vocal Arts Medicine: A Random Forest Application for "Fach" Classification in Opera

Area of Science:

• Vocal arts medicine
• Music performance science
• Computational acoustics

Background:

• Professional voice disorders impact performing artists, particularly opera singers.
• Incorrect "Fach" (voice type) determination can lead to chronic overuse and vocal fold damage.
• Objective fach counseling is needed to prevent career-ending vocal issues.

Purpose of the Study:

• To develop an objective fach counseling method for opera singers.
• To utilize digital sound analyses and machine learning for voice classification.
• To improve diagnostic tools in vocal arts medicine and singing pedagogy.

Main Methods:

• Compiled a database of 2004 sound samples from professional opera singers.
• Employed the Random Forest algorithm, an ensemble learning method, for fach classification.
• Trained the model on acoustic features extracted from voice samples.

Main Results:

• Developed an efficient fach classifier with approximately 80% accuracy for lyric vs. dramatic voice types.
• The system successfully classifies voice samples based on learned acoustic features.
• Demonstrated the potential of machine learning in objective voice analysis.

Conclusions:

• The developed machine learning system offers improved, objective fach counseling for opera singers.
• This approach can help prevent vocal fold damage and premature career termination.
• Further AI-driven methods are being explored to enhance vocal arts medicine diagnostic tools.