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

For singers of all experience levels, one of the most fun and 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). Contemporary Pop and Musical Theater solos increasingly demand strong command through and above the first passagio, and the use of various timbre and textures to achieve a desired quality. Thus, it can be difficult to identify what vocal register within the vocal range a singer is using even for advanced vocalists. This paper presents two methods for classifying vocal registers in an audio signal of male pop music through the end-to-end 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 shows promise for robust classification possibilities across a greater range of voice types and genre.