Aligning Generative Music AI with Human Preferences: Methods and Challenges
This work targets the problem of creating more human-aligned music AI for applications like interactive composition tools, but it is incremental as it builds on existing preference alignment techniques.
The paper addresses the misalignment between generative music AI systems and human preferences due to their loss functions, advocating for preference alignment techniques to bridge this gap. It reviews methods like MusicRL and DiffRhythm+ to tackle challenges such as temporal coherence and subjective quality in music generation.
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.