Acoustic Structure of Laryngeal-Position Stability, Resonance, and Vocal Openness in Classical Singing and Their Associations with Machine-Learning-based Pedagogical Ratings.

BACKGROUND

Key concepts in classical vocal technique, including laryngeal-position stability, resonance, and vocal openness, are central to voice teaching but remain largely dependent on auditory judgment. Their relationships with reproducible acoustic patterns remain insufficiently understood.

OBJECTIVE

This study investigated whether interpretable acoustic features could be associated with expert ratings of these three pedagogical dimensions using machine-learning models and examined acoustic changes following Combined Vocal and Core Muscle Stability (C-VaC) warm-up.

METHODS

A dataset of 3000 audio samples from 12 trained female singers, including paired recordings before and after C-VaC warm-up, was analyzed. Acoustic features were extracted, and separate support vector machine (SVM) models were developed to predict expert ratings on a 1-5 ordinal scale. Model performance was evaluated using subject-independent cross-validation.

RESULTS

Under 10-fold cross-validation, the models achieved an overall macro-F1 score of 0.74 and an accuracy of 0.72. The most consistent acoustic improvements after warm-up were observed in the middle register, including reduced vocal perturbation and increased cepstral peak prominence.

CONCLUSIONS

Multidimensional acoustic features can help model expert-aligned ratings of key concepts in classical vocal pedagogy. This approach may complement traditional auditory evaluation with quantitative information, although further validation in larger and more diverse singer populations is needed.