Hierarchical Generative Modeling of Melodic Vocal Contours in Hindustani Classical Music

TL;DR

This paper introduces GaMaDHaNi, a hierarchical generative model for Hindustani vocal melodies that captures rich melodic nuances using a finely quantized pitch contour representation, improving musical fidelity and interaction capabilities.

Contribution

It presents a novel hierarchical modeling approach using pitch contours as an intermediate representation for Hindustani singing, enhancing expressive melody generation.

Findings

Model outperforms non-hierarchical audio models in capturing pitch contours.

Hierarchical approach improves musical fidelity in generated melodies.

Potential for better human-AI musical collaboration through primed generation and pitch conditioning.

Abstract

Hindustani music is a performance-driven oral tradition that exhibits the rendition of rich melodic patterns. In this paper, we focus on generative modeling of singers' vocal melodies extracted from audio recordings, as the voice is musically prominent within the tradition. Prior generative work in Hindustani music models melodies as coarse discrete symbols which fails to capture the rich expressive melodic intricacies of singing. Thus, we propose to use a finely quantized pitch contour, as an intermediate representation for hierarchical audio modeling. We propose GaMaDHaNi, a modular two-level hierarchy, consisting of a generative model on pitch contours, and a pitch contour to audio synthesis model. We compare our approach to non-hierarchical audio models and hierarchical models that use a self-supervised intermediate representation, through a listening test and qualitative analysis. We also evaluate audio model's ability to faithfully represent the pitch contour input using Pearson correlation coefficient. By using pitch contours as an intermediate representation, we show that our model may be better equipped to listen and respond to musicians in a human-AI collaborative setting by highlighting two potential interaction use cases (1) primed generation, and (2) coarse pitch conditioning.