Vol. 1 No. 4 (2026)

Articles

Online Preference Elicitation for Personalized Singing Synthesis: A Comprehensive Framework for User-Centric Acoustic Adaptation

Published 2026-08-20

  • Joseph Smith
    • ,
  • Abigail Martinez

Joseph Smith
School of Communication Sciences and Disorders, Dalhousie University, Halifax, Nova Scotia, Canada

Abigail Martinez
School of Communication Sciences and Disorders, Dalhousie University, Halifax, Nova Scotia, Canada

Language, Knowledge and Intelligent Systems, Volume 1, Issue 4, 2026 cover

Abstract

The rapid advancement of artificial intelligence in audio generation has significantly improved the naturalness and intelligibility of singing synthesis systems. However, a critical gap remains in aligning the generated vocal performances with the highly subjective and individualized preferences of different users. Conventional systems rely on static datasets and generalized models that output standardized acoustic features, largely ignoring idiosyncratic tastes regarding vibrato depth, breathiness, tension, and emotional delivery. This paper presents a comprehensive conceptual framework and methodology for online preference elicitation designed specifically for personalized singing synthesis. By integrating human-in-the-loop machine learning techniques, the proposed system dynamically interacts with the user, presenting iteratively generated vocal samples and capturing feedback in real time to update an underlying preference model. We explore the parameterization of acoustic features relevant to vocal aesthetics and detail the active learning strategies employed to minimize user fatigue while maximizing the convergence rate of the preference vector. Through detailed architectural design and extensive experimental validation involving both subjective perceptual evaluations and objective convergence metrics, we demonstrate that online preference elicitation significantly outperforms static personalization methods. The findings indicate that users achieve a higher degree of satisfaction and perceived control over the synthesis output. This research bridges the gap between sophisticated generative audio models and human-computer interaction, paving the way for intuitive, accessible, and deeply personalized creative tools in music production.

Keywords

Personalized Singing Synthesis, Online Preference Elicitation, Active Learning, Human-Computer Interaction

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