Aligning Generative Music AI with Human Preferences: Methods and Challenges

TL;DR

This paper reviews methods for aligning generative music AI with human preferences, highlighting recent breakthroughs, challenges, and future directions to improve subjective quality and applicability.

Contribution

It systematically discusses recent preference alignment techniques in music AI, emphasizing their potential and challenges for better human-AI musical collaboration.

Findings

Preference learning improves subjective music quality

Diffusion-based frameworks enable multi-preference alignment

Inference-time optimization enhances personalized music generation

Abstract

Recent advances in generative AI for music have achieved remarkable fidelity and stylistic diversity, yet these systems often fail to align with nuanced human preferences due to the specific loss functions they use. This paper advocates for the systematic application of preference alignment techniques to music generation, addressing the fundamental gap between computational optimization and human musical appreciation. Drawing on recent breakthroughs including MusicRL's large-scale preference learning, multi-preference alignment frameworks like diffusion-based preference optimization in DiffRhythm+, and inference-time optimization techniques like Text2midi-InferAlign, we discuss how these techniques can address music's unique challenges: temporal coherence, harmonic consistency, and subjective quality assessment. We identify key research challenges including scalability to long-form compositions, reliability amongst others in preference modelling. Looking forward, we envision preference-aligned music generation enabling transformative applications in interactive composition tools and personalized music services. This work calls for sustained interdisciplinary research combining advances in machine learning, music-theory to create music AI systems that truly serve human creative and experiential needs.