Machine Learning Approaches to Vocal Register Classification in Contemporary Male Pop Music

TL;DR

This paper introduces two machine learning methods, SVM and CNN, for classifying vocal registers in male pop music using mel-spectrogram features, aiding singers and vocal analysis tools.

Contribution

It presents novel machine learning approaches for vocal register classification in pop music, including practical integration and a new software tool called AVRA.

Findings

Support Vector Machine achieved high classification accuracy.

Convolutional Neural Network demonstrated robust performance.

Methods support broader voice type and genre analysis.

Abstract

For singers of all experience levels, one of the most daunting challenges in learning technical repertoire is navigating placement and vocal register in and around the passagio (passage between chest voice and head voice registers). Particularly in pop music, where a single artist may use a variety of timbre's and textures to achieve a desired quality, it can be difficult to identify what vocal register within the vocal range a singer is using. This paper presents two methods for classifying vocal registers in an audio signal of male pop music through the analysis of textural features of mel-spectrogram images. Additionally, we will discuss the practical integration of these models for vocal analysis tools, and introduce a concurrently developed software called AVRA which stands for Automatic Vocal Register Analysis. Our proposed methods achieved consistent classification of vocal register through both Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models, which supports the promise of more robust classification possibilities across more voice types and genres of singing.